data science engineer

Apple

<p>The Health Technologies Team conceives and proves out exciting new technology for Apple&rsquo;s future products and features. In this role, you can be involved in the end-to-end new product development cycle for health and wellness technologies, from product ideation and feasibility assessment to algorithm implementation and product validation. Join our team to create extraordinary features that represent the cutting edge of innovation in the dynamic consumer health field.<br>
<br>
<b>Description</b><br>
<br>
In this role, you&rsquo;ll collaborate with multidisciplinary teams to create innovative health products and features. As a biomedical data scientist, your responsibilities will include: -design and support of lab and human studies ranging from small scale pilot investigations to large scale sensor fusion studies -distill and interpret study findings to assess system performance and confounders -define and evaluate sensor feasibility criteria, including KPI development, mapping user experience requirements to sensor specifications -develop analysis tools to evaluate and interpret physiological time series sensor data -design, implement, and validate physiological models and algorithms -develop data visualization strategies for sharing complex data and study findings to influence project decisions and direction. The role requires effective collaboration with team members spanning a broad range of expertise and disciplines. Flexible thinking, adaptability to change, comfort with ambiguity, and ability to work both independently as well as in a team setting are hallmarks of success on our team.<br>
<br>
<b>Preferred Qualifications</b><br>
<br>
PhD in Biomedical engineering or other engineering discipline with relevant prior experience.<br>
<br>
Experience with deep learning frameworks (e.g., PyTorch) including model training, loss function design, optimization, and cross-validation.<br>
<br>
Experience with statistical testing methods and their application to experimental data analysis.<br>
<br>
Experience with distributed processing frameworks (e.g., Spark, Dask) for large-scale data workflows.<br>
<br>
Experience with data management, organization, storage, and retrieval.<br>
<br>
<b>Minimum Qualifications</b><br>
<br>
MS in Biomedical engineering or other engineering discipline with relevant prior experience with time-series physiological sensors, devices, and applications.<br>
<br>
Must have a strong understanding of human physiology coupled with experience in the use of multi-sensor systems to measure, characterize, and analyze time-series physiological signals.<br>
<br>
Must have experience using Python to process, analyze, and visualize data. Must be facile with the corresponding Python tools (e.g., matplotlib, plotly, tableau, etc.).<br>
<br>
Must be highly organized and able to thrive in a fast-paced environment. Must have excellent communication skills.&quot;,&quot;internalDetails&quot;:null<br>
<br>
<b>Pay &amp; Benefits</b><br>
<br>
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $115,710 and $203,000 annualized, and your base pay will depend on your skills, qualifications, experience, and location.<br>
<br>
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits<br>
<br>
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.</p>

Biomedical Data Science Engineer - Health Technologies

Chevron

<div><div><p>Chevron believes the future of energy is lower carbon. And we know that affordable, reliable, ever-cleaner energy is essential to achieving a more prosperous and sustainable world. For Chevron, reducing the carbon intensity of oil and gas represents a tremendous opportunity to advance the global net zero ambitions of the Paris Agreement and build a lower-carbon economy.</p><p></p><p>
Technology will play a crucial role in achieving this goal, and Chevron is seeking interns with a desire to thrive in a cutting-edge digital, global environment to securely advance the global energy transition. We differentiate ourselves through the application of technology, taking an approach that includes in-house expertise, proprietary solutions, and strategic partnerships.</p><p></p><p>
You will be supported by mentoring programs and employee networks to excel your professional development, learn about our business and technology organizations from key leaders, and have fun with work and social events throughout the summer!</p><p></p><p>
Information and Analytics is one of five job families within Information Technology. Roles within this job family understand the use of data, insights, and information to support work processes and strategic business objectives:</p><ul><li>
Transforms data into insights for making better decisions across the enterprise.</li><li>
Develops, executes, and manages information and data architectures, governance, practices, and procedures.</li><li>
Manages the full information and data lifecycle needs of the enterprise.</li></ul><p></p><p><b>
Positions available</b></p><ul><li>
Data Scientist</li><li>
Data Engineer</li><li>
Machine Learning Engineer</li><li>
Business Intelligence Analyst</li><li>
Data Analyst</li></ul><p></p><p><b>
Locations</b></p><ul><li>
Houston, TX</li></ul><p></p><p><b>
Data Scientist</b></p><p>
We are looking for motivated individuals to apply data science and machine learning expertise and solution delivery to join our team! You will be responsible for partnering across the business to identify opportunities to apply data science and create data science products that deliver strong value. Successful candidates will have the skills to identify, prototype, and build data science products.</p><p></p><p><b>
Key responsibilities</b></p><ul><li>
Identify and frame opportunities to apply advanced analytics, modeling, and related technologies to data that provide insight and improve decision making, and automation</li><li>
Identify data necessary and appropriate technology to solve business challenges</li><li>
Clean data, develop models, and test models</li><li>
Establish the life cycle management process for models</li><li>
Provide technical mentoring in modeling and analytics technologies, the specifics of the modeling process, and general consulting skills</li></ul><p></p><p><b>
Data Engineer</b></p><p>
We are looking for motivated individuals to design data products and data pipelines that are resilient to change, modular, flexible, scalable, reusable, and cost effective. You will leverage data and software engineering techniques to create business value through data accessibility. The primary role of data engineers is to make data available, secure, and accessible in usable formats to stakeholders. Successful candidates will understand the business use of data and the stakeholders&rsquo; requirements to support work processes and strategic business objectives.</p><p></p><p><b>
Key responsibilities</b></p><ul><li>
Identify, acquire, cleanse/prepare, store data, and develop reusable data products aligned with defined architecture patterns</li><li>
Create and manage data pipelines that enable advanced analytics models, and handle data challenges and opportunities</li><li>
Ensure the scalability and reliability of model deployment, and document the technical aspects of the process</li><li>
Develop and share reusable tools for data engineering tasks, and leverage technical services to optimize data workflows</li></ul><p></p><p><b>
Machine Learning Engineer</b></p><p>
We are looking for motivated individuals to bring technical expertise, innovation, and passion for building amazing data science solutions with real-world impact to join our team! Successful candidates will drive significant value through the agility of a small startup while ensuring scale and model/code reuse to a larger federated, world-wide organization by leveraging agile practices, continuous delivery, and automation techniques. There will be no shortage of opportunities to lead, innovate, challenge the status quo, and work directly with Data Scientists, Analytics Professionals and Business experts to build and deliver innovative, value driven AI solutions.
</p><p></p><p><b>Key responsibilities</b></p><ul><li>
Consult, identify and frame opportunities to implement AI solutions that help gain insight and improve decision making and automation</li><li>
Identify data, technology, and architectural design patterns to solve business challenges using analytical tools and AI design patterns and architectures</li><li>
Partner with Data Scientists and Chevron IT Foundational services to implement complex algorithms and models into enterprise scale machine learning pipelines
</li><li>Build machine and deep learning systems optimized for scalability and performance</li><li>
Transform data science prototypes into scalable solutions in a production environment</li><li>
Orchestrate and configure infrastructure that assists Data Scientists and analysts in building low latency, scalable and resilient machine learning, and optimization workloads into an enterprise software product</li><li>
Run machine learning experiments and fine-tune algorithms to ensure optimal performance</li></ul><p></p><p><b>
Business Intelligence Analyst</b></p><p>
We are looking for motivated individuals to provide data science and analytics expertise and solution delivery across Chevron to join our team! You will understand data in the larger context of the business and the value chain to transform data into insights for better decision-making through scientific exploration, analytics, and data. Successful candidates will be accountable for leading initiatives by performing data analysis, translating data requirements to technical requirements, and driving insights to business problems and decisions.</p><p></p><p><b>
Key responsibilities</b></p><ul><li>
Access, gather, and analyze data from source systems</li><li>
Help frame the business problem by providing quantitative and qualitative data analysis (data quality, availability, etc.)</li><li>
Drive insights to business problems by visualizing the data and telling a story through data (report patterns, trends, anomalies, etc.)</li><li>
Participate in the end-to-end product development lifecycle as a member of agile team</li><li>
Contribute to data analysis, data wrangling, data visualization, and acceptance testing</li><li>
Present findings and new development to help refine backlog items</li></ul><p></p><p><b>
Data Analyst</b></p><p>
We are looking for motivated individuals to provide expertise on how business workflows map to data, and how data can be integrated to build reusable data products. Successful candidates will be able to analyze the current state of data management: process, practices, tools &ndash; helping to shape future needs and serve as a subject matter expert for delivery teams.</p><p></p><p><b>
Key responsibilities</b></p><ul><li>
Understand the business use of data and stakeholder requirements to support strategic business objectives</li><li>
Collaborate with delivery teams to provide data management direction and support for initiatives and product development</li><li>
Contribute to the design of common information models</li><li>
Consult on the appropriate data integration patterns, data modeling and data quality</li><li>
Maintain and share knowledge of requirements, key data types and data definitions, data stores, and data creation process</li></ul><p></p><p><b>
Requirements</b></p><p><b>
Preferred education / degrees</b></p><ul><li>
Currently enrolled in bachelor&rsquo;s or master&rsquo;s degree program in Computer Science, Computer Engineering, Mathematics, Statistics, Operations Research, Data Science, Management Information Systems, or related Engineering degree</li><li>
Must provide a current, unofficial transcript with online resume (as proof of good academic standing) when applying for this position to be considered.</li></ul><p></p><p><b>
Experience</b></p><ul><li>
Data acquisition, analysis, modeling, movement, transformation, and preparation experience</li><li>
Demonstrated depth in advanced analytics / data science technologies (e.g., machine learning, operations research, statistics, data mining)</li></ul><ul><li>
Data Analyst: Experience with data modeling, data management, data quality, SQL</li><li>
Data Engineer: Experience using data pipelines, Data Lake and storage configuration, Python, RDBMS &amp; SQL</li><li>
Machine Learning Engineer: Software Engineering background. Working knowledge of mathematics (primarily linear algebra, probability, statistics), and algorithms. Working knowledge of machine learning frameworks and machine learning libraries.</li></ul><p></p><p><b>
Preferred skills / competencies</b></p><ul><li>
Ability to communicate in a clear and concise manner both orally and in writing.</li></ul><ul><li>
Knowledge of enterprise SaaS complexities including security/access control, scalability, high availability, concurrency, online diagnoses, deployment, upgrade/migration, internationalization, and production support</li><li>
Experience designing custom APIs for machine learning models for training and inference processes</li><li>
Software engineering skills and fundamentals: coding (Python, R) and Github, source control versioning, requirement spec, architecture, and design review, testing methodologies, CI/CD, etc.</li></ul><p></p><p><b>
Additional information</b></p><ul><li>
Relocation may be considered within Chevron parameters</li><li>
Chevron regrets that it is unable to sponsor employment visas or consider individuals on time limited visa status for this position</li></ul></div><div></div><div><div><div><div><p><b><br>
Regulatory Disclosure for US Positions:</b></p><ul><li>
The compensation and reference to benefits for this role is listed on this posting in compliance with applicable law.
</li><li>The anticipated hourly range for this position is $23.65 - $37.50. The selected candidate&rsquo;s monthly or hourly compensation will be determined based on their respective discipline, degree level and academic year for U.S. payroll offers.
</li><li>Chevron offers competitive compensation and benefits programs to eligible employees which includes, but is not limited to, variable pay, health care coverage, retirement plan, protection coverage, time off and leave programs, training and development opportunities and a range of allowances connected to specific work situations. Details are available at http://hr2.chevron.com/.</li></ul></div></div></div></div><p></p><p><br>
U.S. Regulatory notice:</p><p></p><p>
Chevron is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, sex (including pregnancy), sexual orientation, gender identity, gender expression, national origin or ancestry, age, mental or physical disability, medical condition, reproductive health decision-making, military or veteran status, political preference, marital status, citizenship, genetic information or other characteristics protected by applicable law.</p><p></p><p>
We are committed to providing reasonable accommodations for qualified individuals with disabilities. If you need assistance or an accommodation, please email us at emplymnt@chevron.com.</p><p></p><p>
Chevron participates in E-Verify in certain locations as required by law.</p></div>

2026-2027 Information Technology – Information and Analytics – Intern

Openkyber

<div><p>OpenKyber is a Proven Leader in providing full IT Services, Software Development and Solutions for 15 years. OpenKyber is a Small &amp; Woman Owned Minority Business with GSA Advantage Certification. We have offices in VA; MD &amp; Offshore development centers in India. We have successfully executed 100+ projects for clients ranging from small business and non-profits to Fortune 50 companies and federal, state and local agencies.</p>
<b>Job Role:</b> Senior DevOps Engineer (Kubernetes)<br>
<b>Location:</b> Camden, NJ - Hybrid (3 days/week onsite) only locals<br>
<b>Duration:</b> Long-Term<br>
<b>About the Role</b>
<p>The Data Scientist plays a pivotal role in planning, executing, and delivering machine learning-based projects that drive business impact. This role involves analyzing large datasets, developing AI /ML /optimization models, and translating findings into actionable insights. The Data Scientist partners with business and operational leaders, supports senior leadership with analytics, and fosters a culture of data-driven decision-making across the organization.</p>
<b>Key Responsibilities</b>
<ul><li>Collect, clean, and analyze datasets from diverse internal and external sources, applying advanced data wrangling techniques to handle structured, semi-structured, and unstructured data while ensuring completeness, consistency, and accuracy.</li>
<li>Acquire access to various databases and source systems (SQL, NoSQL, graph databases) and create data pipelines for efficient and repeatable data science projects.</li>
<li>Apply statistical analysis and visualization techniques (hierarchical clustering, principal components analysis (PCA)) to explore and prepare data.</li>
<li>Design, develop, and validate machine learning, statistical, and optimization models for classification, regression, clustering, recommendation, and prediction tasks.</li>
<li>Select appropriate algorithms and models for AI /ML, and rigorously test them for accuracy, robustness, and fairness.</li>
<li>Perform feature selection and engineering, create predictive variables, and experiment with transformations to enhance performance and interpretability.</li>
<li>Integrate domain knowledge into ML solutions (e.g., care delivery, financial risk, customer journey, quality prediction, sales, marketing).</li>
<li>Conduct controlled experiments (A/B and multivariate testing), to evaluate hypotheses, measure workflow changes, and quantify the impact of AI solutions on operations.</li>
<li>Collaborate with MLOps, data engineers, and IT to evaluate deployment options, and establish best practices around ML production infrastructure.</li>
<li>Continuously monitor execution and health of production ML models, recalibrating as needed and updating them to reflect new data or changing business conditions.</li>
<li>Work with cross-functional teams, collaborating with stakeholders to refine objectives, and ensure alignment between technical outputs and strategic goals.</li>
<li>Create dashboards, and interactive visualizations that communicate results to a wide range of audiences, turning technical findings into actionable recommendations.</li>
<li>Communicate complex projects, models, and results to diverse audiences, including executives and frontline staff, using storytelling and presentation techniques.</li>
<li>Stay current with industry research and emerging technologies in AI, machine learning, and optimization, proactively experimenting with new methods and recommending adoption of tools that strengthen analytics capabilities.</li>
<li>Mentor junior data scientists and analysts, provide guidance on technical approaches and model interpretation, and promote collaboration across teams.</li>
</ul><b>Qualifications</b>
<p>Education Master's, or PhD in Computer Science, Data Science, Engineering, Statistics, Applied Mathematics, Operations Research, or a related quantitative field. Specialization in ML, AI, cognitive science, or data science is highly preferred.</p>
<p>Experience and Skills 3-5 years of hands-on experience planning and executing end-to-end data science projects with demonstrated impact on clinical or operational outcomes in business environments Advanced programming proficiency in Python or R with strong expertise in machine learning frameworks (scikit-learn, TensorFlow, PyTorch) and statistical analysis tools Expertise in machine learning and statistical techniques including supervised/unsupervised learning, deep learning, NLP, computer vision, regression models, ensemble methods, and experimental design (A/B testing) Strong data engineering capabilities including SQL/NoSQL database programming, distributed computing tools (Hadoop, Spark, Kafka), data pipeline development, and experience with cloud platforms (AWS, Azure, Google Cloud Platform) Production ML and MLOps experience including model deployment, monitoring, containerization (Docker, Kubernetes), version control, and applying DevOps principles to data science workflows Data visualization and communication excellence with ability to create compelling dashboards (Tableau, Power BI), translate complex technical findings into actionable insights, and present to diverse audiences from executives to frontline staff Cross-functional collaboration skills with proven ability to work in agile environments, partner with stakeholders to align technical solutions with business objectives, and mentor junior team members Healthcare domain knowledge preferred, particularly experience with Epic EHR systems, clinical workflows, and healthcare data standards, along with relevant certifications (Clarity /Caboodle, Google Cloud ML Engineer, AWS ML Specialist)</p>
<p><b>For applications and inquiries, contact:</b> hirings@openkyber.com</p></div>

Senior DevOps Engineer (Kubernetes)

Tata Consultancy Services (TCS)

<div></div><div><div><div><div><div>Role - Data Scientist(Stats Modelling)
</div><div>Experience Required - 7+ Years</div><br>
<div><br>
</div><div>Must Have Technical/Functional Skills</div><br>
<div><br>
</div><div>Programming: Proficiency in Python, R, and SQL.
</div><div>Mathematics/Statistics: Strong understanding of statistical techniques, probability, and linear algebra.
</div><div>Machine Learning: Knowledge of algorithms and libraries like Scikit-learn, TensorFlow, or PyTorch.</div><br>
<div><br>
</div><div>Roles &amp; Responsibilities</div><br>
<div><br>
</div><div>Core Roles and Responsibilities
</div><div>Work with the Insurance Data Science teams to develop, refine, test, and deliver model monitoring capabilities. This will include building prototype data assets, calculating metrics, constructing visualizations/dashboard prototypes, and engaging in technical review with AI/ML Engineers &amp; Data Scientists.
</div><div>Looking for model ops and model monitoring support. A scientist with strong cs/engineering skills or an engineer with strong science/analytics experience. Here are the specifics we are looking for:
</div><div>The candidate may also be called upon to perform ad-hoc data/analysis requests, manage scheduled jobs in the Domino platform, collaborate with AI/ML engineers, and other as needed tasks for improving Model Ops in the Insurance Data Science team.</div><br>
<div><br>
</div><div>1. Leverage understanding of models and collaborate with Data Scientists to refactor the code into IT maintainable solutions that follows best practices and meets appropriate coding standards.
</div><div>2. Read model development and ongoing monitoring documentation and engage with data scientists to design the needed monitoring solution for each model.
</div><div>3. Read and optimize Model development and production SQL queries to develop ground truth data sets for model monitoring activities.
</div><div>4. Design and develop prototype tables that align to USAA's data modeling and DDLC requirements for seamless traditionalization by USAA IT.
</div><div>5. Conduct thorough quality checks to ensure the accuracy and reliability of the data and processes including unit testing, run-to-run testing, etc.
</div><div>6. Code and implement solutions primarily in Python, ensuring optimal performance and scalability.
</div><div>7. Run and interpret completed model monitoring dashboards to monitor for breeches in performance and notify the respective data scientists.
</div><div>8. Work with data scientists to incorporate monitoring results into Quarto based documentation.</div><div><br>
</div>
<div></div><br>
<div>Base Salary Range : $100,000 to $140,000 Per Annum</div><br>
<div><br>
</div><div>TCS Employee Benefits Summary:
</div><div>Discretionary Annual Incentive.
</div><div>Comprehensive Medical Coverage: Medical &amp; Health, Dental &amp; Vision, Disability Planning &amp; Insurance, Pet Insurance Plans.
</div><div>Family Support: Maternal &amp; Parental Leaves.
</div><div>Insurance Options: Auto &amp; Home Insurance, Identity Theft Protection.
</div><div>Convenience &amp; Professional Growth: Commuter Benefits &amp; Certification &amp; Training Reimbursement.
</div><div>Time Off: Vacation, Time Off, Sick Leave &amp; Holidays.
</div><div>Legal &amp; Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.
</div></div></div></div><div><div><div><b>Location</b></div>
<div>San Antonio, TX</div>
</div><div><div><b>Job Function</b></div>
<div>TECHNOLOGY</div>
</div><div><div><b>Role</b></div>
<div>Data Scientist</div>
</div><div><div><b>Job Id</b></div>
<div>411325</div>
</div><div><div><b>Desired Skills</b></div>
<div>Data Science | Statistics</div>
</div><div><div><b>Salary Range</b></div>
<div>$100,000-$140,000 a year</div></div></div></div><div></div>

Data Scientist

Quantifind

<div>
 <strong>
  Who You Are
  <br/>
  <br/>
 </strong>
 You are a quantitative thinker who wants to develop further as both a data scientist and an engineer. You are skilled at finding the precise mathematical kernels of real-world problems and want to bring that talent to bear on the business questions facing the world&rsquo;s leading financial services companies. You are excited to apply your existing expertise in fields such as statistics and computer science to solve mission critical problems. You are excited to work at a fast-paced startup where you will have a chance to expand your scientific and engineering skills to new areas. You share Quantifind&rsquo;s commitment to winning together, and are eager to see your coworkers build on the technical foundations you will be creating. You are passionate about maintaining the high scientific and engineering standards required to enable your peers. Above all, you are a curious and independent problem solver who is motivated to find a place where your skills can have real impact.
 <br/>
 <br/>
 <strong>
  Who We Are
  <br/>
  <br/>
 </strong>
 Quantifind helps some of the world&rsquo;s biggest banks catch money laundering and fraud. Quantifind also works with government agencies to use the same platform to uncover criminal networks and combat money laundering committed by internationally sanctioned entities. Unlike other players in this space, Quantifind delivers results as Software-as-a-Service (SaaS) with consumer-grade user experiences.
 <br/>
 <br/>
 Quantifind is a data science technology company whose AI platform uncovers signals of risk across disparate and unstructured text sources. In financial crimes risk management, Quantifind&rsquo;s solution uniquely combines internal financial institution data with public domain data to assess risk in the context of Know Your Customer (KYC), Customer Due Diligence (CDD), Fraud Risk Management, and Anti-Money Laundering (AML) processes. Today these compliance processes are burdened by ever-increasing regulatory responsibilities and an expectation of frictionless transactions. Legacy technologies demand increasingly more human resources as the operations expand; Quantifind&rsquo;s solution offers a way to cut through the inefficiency and enhance effectiveness through Machine Learning driven solutions that resolve for both accuracy and relevance.
 <br/>
 <br/>
 To help you succeed, we provide a supportive environment that fosters collaboration between teams and team members, where learning and professional growth are considered a key part of your success, and of ours. We offer a flexible work environment with a family friendly work-life balance.
 <br/>
 <br/>
 <strong>
  What You'll Be Doing
  <br/>
  <br/>
 </strong>
 Members of the Data Science team carry solutions all the way through initial scoping of the problem, performing exploratory analysis, model training, and finally putting highly performant implementations into production. As a team member your duties will include:
 <br/>
 <br/>
 <ul>
  <li>
   Collaborating with fellow team members and key stakeholders, such as, Product Managers, Platform Engineers to explore ideas, test hypotheses, and prototype solutions.
  </li>
  <li>
   Working in an agile environment breaking down complex problems into smaller manageable tasks with the help of your teammates and manager.
  </li>
  <li>
   Leveraging SQL, Python and PySpark to analyze large unstructured data sets to answer key business questions, inform next steps or set up modeling tasks.
  </li>
  <li>
   Writing complex pipeline code often involving NLP enrichment steps to process large data sets.
  </li>
  <li>
   Training machine learning models to solve complex problems.
  </li>
  <li>
   Productionizing models in a Scala codebase following software engineering best practices.
  </li>
  <li>
   Validating models using standard and custom performance metrics.
  </li>
  <li>
   Leveraging Large Language Models (LLMs) to scale up various data science tasks (for example: data labeling, extracting structured information, &hellip;)
  </li>
  <li>
   Participating in code reviews.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  What A Great Candidate Looks Like
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   MS or higher in the following areas: Statistics, Mathematics, or Computer Science
  </li>
  <li>
   At least 1 year of professional industry experience, in addition to your academic experience
  </li>
  <li>
   Outstanding analytical skills and the ability to communicate quantitative results to technical and non-technical audiences
  </li>
  <li>
   Strong knowledge of Statistics/Probability/Machine Learning including core concepts of hypothesis testing, inference, bias-variance trade-off, regularization, dimensionality reduction etc.
  </li>
  <li>
   Supervised and unsupervised statistical techniques such as regression, classification, and clustering
  </li>
  <li>
   Experience with popular machine learning algorithms such as random forests, Boosting, and neural networks
  </li>
  <li>
   Experience with Applied NLP methods such as topic modeling, text classification, word embeddings, and named entity extraction is a strong plus
  </li>
  <li>
   Strong programming experience in Python and one or more of the following: Scala/Java or R
  </li>
  <li>
   Experience with SQL and/or Spark
  </li>
  <li>
   Knowledge of data structures and algorithm complexity
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  The Opportunity We Offer
  <br/>
  <br/>
 </strong>
 Quantifind is seeking to fill an Associate Data Scientist position on our Data Science team. The Data Science team works closely with the Product Management team and Platform engineers to anticipate company needs and quickly put state-of-the-art mathematical tools into the hands of end users. Our development team is centered in Palo Alto, California, and we have technology hubs in Atlanta, Georgia, and in Washington, D.C. We are currently using a hybrid mix of working from home and in the office. The DC team is primarily remote, with periodic in person touchpoints at a physical office space in DC.
 <br/>
 <br/>
 <strong>
  A Highlight Of Our Benefits
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Competitive salary
  </li>
  <li>
   Company Equity
  </li>
  <li>
   Exceptional benefits package
  </li>
  <li>
   Flexible Vacation &amp; Paid Time Off
  </li>
  <li>
   Employer-matched 401(k) plan
  </li>
  <li>
   A fun environment where work-life balance is valued
   <br/>
   <br/>
  </li>
 </ul>
 The base salary range for this full-time position is $100,000-$130,000. Our salary ranges are determined by role, level, and location, and the range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location, role-related knowledge and skills, depth of experience, relevant education or training, and additional role-related considerations.
 <br/>
 <br/>
 Depending on the position offered, equity, bonuses, commission, or other forms of compensation may also be provided as part of a total compensation package, in addition to a full range of medical, financial, and other benefits.
 <br/>
 <br/>
 We are an equal opportunity employer. We pride ourselves on living our values. We are curious. We respect each other. We are proactively transparent. We relentlessly solve problems. We win together.
 <br/>
 <br/>
 Will you join us? Apply now!
</div>


Associate Data Scientist

Samba TV

<div>
 <p>
  <strong>
   Samba is a media intelligence company. We know what the world is watching, reading, and thinking about &amp;mdash; in real time, at scale, across every screen. Our data exists with the consent of over a billion people, organized into the most complete picture of consumer attention ever built. The biggest brands in the world use that picture to make smarter decisions. We think it&rsquo;s the most interesting data asset on the planet, because it&rsquo;s the most culturally relevant.&nbsp;
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <br/>
  <p>
   <strong>
    <span>
     ABOUT THE ROLE
    </span>
   </strong>
  </p>
  <br/>
  <p>
   <span>
    We are looking for a hands-on Data Scientist to own and deliver complex measurement science and modeling work at the core of our measurement and audience sciences products.&nbsp;
   </span>
  </p>
  <br/>
  <p>
   <span>
    The role requires a deep, first-principles understanding of data science and machine learning &amp;mdash; not just the ability to apply libraries, but the ability to reason clearly about model behavior, articulate trade-offs between approaches, and make defensible methodological decisions under ambiguity. This is emphatically a coding role &amp;mdash; you will spend the majority of your time writing production-quality Python, building and evaluating models on large-scale viewership and web data, and delivering end-to-end ML solutions.
   </span>
  </p>
  <br/>
  <p>
   <span>
    You will work closely with Data Engineering, Product, and go-to-market teams.
   </span>
  </p>
  <br/>
 </p>
 <p>
  <p>
   <br/>
  </p>
  <strong>
   WHAT YOU'LL DO
  </strong>
  <br/>
  <ul>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Write and own production-quality Python code end-to-end &amp;mdash; well-structured, tested, documented, and built to last; PySpark proficiency is essential for working with Samba's billion-row viewership datasets
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Design, build, and deploy measurement models and statistical frameworks that power Samba&rsquo;s campaign measurement, reach/frequency estimation, and cross-platform attribution products
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Apply the right statistical and ML technique to the right problem &amp;mdash; drawing from hierarchical models, Bayesian inference, gradient boosting, regularized regression, causal ML, and probabilistic record linkage &amp;mdash; and clearly articulate the reasoning behind your choices
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Build and evaluate multi-touch and multi-channel attribution models; apply Causal ML methods &amp;mdash; counterfactual modeling, meta-learners (S-learner, T-learner, X-learner), and heterogeneous treatment effect estimation &amp;mdash; to advertising and viewership measurement problems
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Partner with Data Engineering to define data requirements, validate pipelines, and ensure model inputs are reliable, scalable, and production-ready
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Lead technical design reviews and contribute meaningfully to architecture decisions across the Data Science team
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Mentor junior Data Scientists through code review, pairing, and structured technical feedback &amp;mdash; raising the team's technical floor
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Communicate measurement methodologies and findings clearly to technical and non-technical audiences, including senior leadership and external clients
     </span>
    </p>
    <br/>
   </li>
   <br/>
  </ul>
  <br/>
  <p>
   <br/>
  </p>
  <strong>
   WHO ARE YOU
  </strong>
  <br/>
  <ul>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      5-7 years of professional data science experience &amp;mdash; hands-on, delivery-focused, and measurable in shipped models and production systems
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Expert-level Python &amp;mdash; clean, modular, testable, production-ready code is your standard, not your aspiration
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Advanced PySpark and Databricks &amp;mdash; comfortable building and optimizing data pipelines and ML workflows on billion-row datasets
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Deep, first-principles command of statistics and ML &amp;mdash; you can explain from the ground up how these models work and you apply this understanding to make better modeling decisions
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Solid grasp of experimental design &amp;mdash; A/B testing, randomization, power analysis, and the conditions under which observational causal inference is appropriate
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Fluent in the full ML lifecycle: feature engineering, model evaluation, deployment pipelines, drift monitoring, and iterative improvement in production
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Hands-on experience with uplift modeling, synthetic control, difference-in-differences, or propensity-based approaches applied to advertising or media outcomes
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Strong ownership mindset &amp;mdash; you drive projects independently and are comfortable owning your models from data exploration through production delivery, with minimal hand-holding.
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Clear communicator &amp;mdash; able to translate statistical reasoning and model behavior into language that drives decisions with product, engineering, and leadership
     </span>
    </p>
    <br/>
   </li>
   <br/>
  </ul>
  <br/>
  <br/>
  <ul>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Experience with multi-touch attribution (MTA) or multi-channel attribution modeling &amp;mdash; understanding of the limitations of rule-based approaches and the methodological trade-offs of data-driven alternatives
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Hands-on experience with Causal ML methods &amp;mdash; counterfactual modeling, meta-learners, and heterogeneous treatment effect estimation &amp;mdash; applied to advertising or media measurement outcomes
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Direct exposure to TV or digital viewership data &amp;mdash; ACR signals, STB data, viewership panels, or cross-platform measurement (linear + CTV/OTT)
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Familiarity with the measurement
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      t vendor landscape (Nielsen, Comscore, VideoAmp, iSpot) and industry standards (MRC, GRP/TRP frameworks)
     </span>
    </p>
    <br/>
   </li>
   <br/>
   <li>
    <br/>
    <p>
     <span>
      Advanced degree (MS or PhD) in Statistics, Mathematics, Computer Science, or a related quantitative field &amp;mdash; or equivalent depth demonstrated through work
     </span>
    </p>
    <br/>
   </li>
   <br/>
  </ul>
  <br/>
  <br/>
 </p>
 <p>
  <br/>
 </p>
 <br/>
 <p>
  <strong>
   Samba is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.&nbsp;&nbsp;We strive to empower connection with one another, reflect the communities we serve, and tackle meaningful projects that make a real impact.
  </strong>
 </p>
 <br/>
 <p>
  &nbsp;
 </p>
 <br/>
 <p>
  <span>
   Samba may collect personal information directly from you, as a job applicant, Samba may also receive personal information from third parties, for example, in connection with a background, employment or reference check, in accordance with the applicable law.
  </span>
  <strong>
   For further details, please see Samba's
  </strong>
  <strong>
   Applicant Privacy Policy
  </strong>
  <strong>
   .
  </strong>
  <strong>
   For residents of the EU , Samba Inc. is the data controller.
  </strong>
 </p>
 <br/>
 <p>
  We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
 </p>
 <br/>
 <br/>
 Samba expects to offer a base salary between
 <strong>
  $150,000 - $180,000 USD
 </strong>
 per year for roles to be performed in San Francisco or California; actual base salary offered will depend on various factors including but not limited to location, experience, and performance. Base salary is just one component of Samba's total compensation package for employees. Other rewards may include bonuses, short-term incentives and long-term incentives. In addition, Samba provides health insurance, wellness offerings, life and disability insurance, a retirement savings plan, paid holidays and paid time off (PTO), and other employee benefits.
</div>


MASTER-WORKS

<div><p><b>Job Description:</b></p><p>
The charge of creating and developing intelligent solutions using Artificial Intelligence technologies with a focus on generative AI and data science.
</p><p></p><p>The role entails converting data into useful insights and developing AI powered applications that improve decision making and enhance operational efficiency.</p><p></p><p><b>
Tools &amp; Technologies: </b>Dataiku, Sql server, Power bi, and aws bedrock</p><p></p><p><b>
Key Responsibilities:</b></p><ul><li>
Design, develop, train, and optimize machine learning models for real applications or use cases.</li><li>
Translate business and product requirements into scalable ML/AI solutions.</li><li>
Implement feature engineering, model selection, tuning, and evaluation techniques.</li></ul><ul><li>
Develop, and deploy ML models into production environments with high availability and performance.</li><li>
Build and maintain ML pipelines (training, validation, deployment, monitoring).</li><li>
Monitor model performance, data drift, and model decay; retrain models as needed.</li><li>
Ensure models meet reliability, scalability, and security standards.</li><li>
Work closely with Data Scientists, Product Managers, and Software Engineers.</li><li>
Collaborate with data engineering teams to ensure high-quality, reliable data pipelines.</li><li>
Participate in design and code reviews, ensuring engineering best practices.</li><li>
Optimize models for latency, throughput, and cost.</li><li>
Implement experimentation frameworks (A/B testing, offline evaluation).</li><li>
Apply responsible AI principles, including fairness, explainability, and governance where required.</li></ul><p></p><b><br>
Requirements</b><p><b><br>
Requirements &amp; Qualifications</b>:</p><ul><li>
+4 years of hands-on experience in Machine Learning or applied AI roles.</li><li>
Strong programming skills in Python (and/or Java, Scala).</li><li>
Solid understanding of ML algorithms (supervised, unsupervised, deep learning).</li><li>
Experience with frameworks such as TensorFlow, PyTorch, Scikit-learn.</li><li>
Experience deploying models using Docker, Kubernetes, or cloud ML services.</li><li>
Strong knowledge of data structures, algorithms, and software engineering principles.</li><li>
Experience working in agile, cross-functional teams.</li><li>
Experience with cloud platforms (AWS, Azure, or GCP) and managed ML services.</li><li>
Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker, Azure ML).</li><li>
Experience with big data technologies (Spark, Kafka, Databricks).</li><li>
Background in NLP, Computer Vision, or Generative AI.</li><li>
Strong problem-solving and analytical thinking</li><li>
Production-first mindset</li><li>
Data-driven decision making</li><li>
High Collaboration and communication skills</li></ul></div>

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