data analytics chief scientist

Data Scientist, Analytics (Technical Leadership)

Peraton

<div><div><h5 class="jobSectionHeader"><b>ABOUT PERATON</b></h5><p>
Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world&rsquo;s leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace. The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees solve the most daunting challenges that our customers face. Visit peraton.com to learn how we&rsquo;re keeping people around the world safe and secure.</p></div><div><h5 class="jobSectionHeader"><b>
ABOUT THE ROLE</b></h5><div><div>
Peraton seeks a <b>Principal Data Scientist</b> to support Department of Defense Cyber Defense Command (DCDC) with DCO AI Cyber Security. Location: Fort Meade, MD. In this role, you will serve as technical lead for the Data Science Team, providing strategic direction for all advanced analytics, AI/ML, and predictive modeling initiatives supporting DCDC enterprise mission requirements.</div><div></div><div><br>
Primary Responsibilities</div><ul><li>
Lead team of 6 data scientists, ML engineers, and operations research analysts</li><li>
Design and oversee AI/ML model development including NLP and network analysis</li><li>
Direct campaigns of learning and experimentation for analytic performance evaluation</li><li>
Develop predictive, prescriptive, and descriptive models for DCO-IDM operations</li><li>
Ensure analytics alignment with ASOM and Analytic Crosswalk framework</li><li>
Present analytic findings and recommendations to senior leadership</li></ul></div></div><div><h5 class="jobSectionHeader"><b>
QUALIFICATIONS</b></h5><div><div>
Required Qualifications:</div><ul><li>
Minimum of 12 years with BS/BA; Minimum of 10 years with MS/MA; Minimum of 7 years with Ph.D.</li><li>
Expert proficiency in Python, R, and ML frameworks (TensorFlow, PyTorch, scikit-learn)</li><li>
Deep experience with NLP, network analysis, and time-series forecasting</li><li>
Strong background in statistical modeling and experimental design</li><li>
Experience with big data platforms (Hadoop, Spark)</li><li>
5+ years leading technical teams in DoD/IC environment</li><li>
U.S Citizenship required</li><li>
DoD TS with ability to obtain/maintain SCI clearance</li></ul><div>
 Preferred Qualifications:</div><ul><li>
AWS/Azure ML certifications are preferred</li><li>
CISSP certification is preferred</li><li>
Master's degree is required; PhD is preferred in Data Science, Statistics, Computer Science, or related field</li><li>
12 years of relevant experience</li></ul></div></div><div><h5 class="jobSectionHeader"><b>
DETAILS</b></h5><div><p>
<b>Target Salary Range:</b> $146,000 - $234,000. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual&rsquo;s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.
</p></div><div><p><b>Benefits Statement:</b> Peraton offers eligible employees a variety of benefits including medical, dental, vision, life, health savings account, short/long term disability, EAP, parental leave, 401(k), paid time off (PTO) for vacation, and company paid holidays. A full listing of available benefits can be viewed at https://www.careers.peraton.com/benefits.
</p></div><div><p>
<b>Application Duration Statement:</b> The application period for the job is estimated to be 30 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates.
</p></div><p><b>
EEO:</b> Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law.</p></div></div><div></div>

PRINCIPAL DATA SCIENTIST

Agency Partner Interactive

<p>We are seeking an experienced Senior Data Analyst with 8 - 10+ years of expertise in business intelligence, advanced analytics, and data visualization. The ideal candidate will be highly skilled in <b>Tableau, Power BI, Python, and SQL (Snowflake)</b>, delivering data solutions that directly support strategic decision-making.</p><p><b>Key Responsibilities</b></p><ul><li>Design, develop, and maintain impactful dashboards and reports using Tableau and Power BI.</li><li>Write and optimize complex SQL queries (Snowflake) for performance and scalability.</li><li>Utilize Python for advanced analytics, automation, and data processing.</li><li>Collaborate with cross-functional teams to understand requirements and deliver data-driven insights.</li><li>Develop data models and ensure accuracy, validation, and consistency across BI solutions.</li><li>Analyze large datasets, uncover trends, and provide recommendations to support business growth.</li><li>Drive continuous improvement in BI and analytics platforms for a better user experience.</li></ul><p><b>Qualifications</b></p><ul><li>Bachelor&rsquo;s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field.</li><li>8&ndash;10+ years of experience in data analytics, BI, and dashboard development.</li><li>Strong expertise in SQL (Snowflake), with deep knowledge of query optimization and performance tuning.</li><li>Proficiency in Tableau and Power BI for building complex, dynamic visualizations.</li><li>Hands-on experience with Python for analytics and automation.</li><li>Excellent analytical thinking, problem-solving, and communication skills.</li></ul><p><b>Preferred Skills</b></p><ul><li>Experience with data modeling and ETL pipelines.</li><li>Exposure to other cloud data warehouses (Redshift, BigQuery).</li><li>Ability to work in a fast-paced, cross-functional environment.</li></ul><p>Job Type: Full-time</p><p>Application Question(s):</p><ul><li>What are your current &amp; gross salary expectations, and is it negotiable?</li><li>If hired, what would be your notice period, and is there any possibility of an early joining date?</li><li>Have you worked with international clientele and their projects? If so, can you share your experience?</li><li>Why are you considering leaving your current role, or why did you leave your previous company?</li><li>Please provide your email address for further communication:</li><li>Which time slot do you prefer: 7:00 PM to 4:00 AM or 5:00 PM to 2:00 AM?</li></ul><p>Work Location: In person</p>

Principal Consultant, Data Analytics

MA'ADEN

<div><p><b>Job Purpose
</b></p><ul><li><div>The Lead Specialist, Data Science &amp; Analytics, acts as a technical leader and senior practitioner, driving development, deployment, and scaling of Machine Learning, AI, and advanced analytics solutions across Maaden.
</div></li><li><div>The role ensures analytics products are designed, validated, industrialized, governed, and adopted at scale, providing measurable value across mining, processing, operations, and enterprise functions.
</div></li><li><div>Lead Specialist, Data Science &amp; Analytics is to analyze data, extract insights, and build predictive models that help organizations make smarter decisions and solve difficult problems. By blending expertise in statistics, computer science, and business strategy, they not only analyze complex datasets but also build predictive models that improve operations and shape long-term decisions. With nearly every industry leaning on data today, the demand for skilled professionals continues to grow<br>
</div></li></ul><p></p><p><b>Key Accountabilities:
</b></p><p><b>1. Lead End-to-End Data Science Delivery
</b></p><ul><li>Developing, implementing and maintaining databases and data collection systems
</li><li>Own the full lifecycle of ML/AI initiatives - from problem framing, data exploration, feature engineering, model development, validation, and MLOps handover.
</li><li>Deliver scalable and production-grade models, ensuring alignment with enterprise data governance and AI standards.
</li><li>Performing statistical analysis to understand and interpret data insights
</li><li>Applying data mining techniques to identify patterns, trends, and relationships in large datasets
</li><li>Building predictive models and machine learning algorithms to forecast future outcomes
</li><li>Creating clear data visualizations and reports to communicate findings to stakeholders
</li><li>Working with cross-functional teams to understand business needs and provide data-driven solutions
</li><li>Design and maintain reliable data pipelines and models in partnership with data engineering to ensure data is accurate, timely, and trustworthy for downstream use
</li><li><div>Ensuring data security and compliance with relevant regulations
</div></li><li><div>Drive experimentation, model versioning, automated retraining, and continuous improvement.<br>
</div></li></ul><div></div><p><b>2. Translate Business Needs into AI/Analytics Solutions
</b></p><ul><li><div>Establish frameworks and operating models that make data science accessible, scalable, and embedded within business and technical functions
</div></li><li><div>Engage BU/domain stakeholders to identify value creation opportunities and convert them into actionable analytics use cases.
</div></li><li><div>Build value hypotheses, KPIs, success criteria, and solution roadmaps in collaboration with Data &amp; AI leadership and business teams.<br>
</div></li></ul><p></p><p><b>3. Industrialize AI/ML Models (ML Ops &amp; Architecture)
</b></p><ul><li>Partner with data engineering, data platforms, and cloud/OT architecture teams to embed models into enterprise systems and operational layers.
</li><li>Set standards for production deployment, testing, monitoring, drift handling, and lifecycle governance.
</li><li>Ensure seamless integration of predictive and optimization models into enterprise platforms, control systems, and digital twins
</li>Leverage machine learning, optimization, and computer vision as enabling tools for performance, reliability, and sustainability improvements<br>
<li>
</li></ul><p></p><p><b>4. Responsible AI, Quality &amp; Governance
</b></p><ul><li>Ensure compliance with Maaden&rsquo;s Responsible AI, data quality, and data governance frameworks.
</li><li>Promote reproducibility, documentation, lineage tracking, and auditability across all data science assets.
</li>Ensure transparency, explainability, and continuous model governance across production and enterprise environments<br>
<li>
</li></ul><p></p><p><b>5. Stakeholder Management &amp; Value Realization
</b></p><ul><li>Communicate insights, results, risks, and recommendations to decision-makers using compelling narratives and visualization.
</li>Track value realization, adoption metrics, and operational impact to ensure measurable benefit.<br>
<li>
</li></ul><p></p><p><b>Minimum Qualification, Experience and Core Competencies:
</b></p><p><b>Minimum Qualifications:
</b></p><ul><li><p>Bachelor&rsquo;s degree in computer science, Data Science, Engineering, Mathematics, Statistics, or related fields.<br>
</p></li></ul><p></p><p><b>Minimum Experience:<br>
</b><br>
<br>
</p><ul><li><div><b>Minimum Experience:
</b></div></li><li><div>8 &ndash; 10 years&rsquo; experience in Data Science / Advanced Analytics with industrial, mining, or heavy-asset environments preferred. Including at least 2 years leading or mentoring analytics professionals
</div></li><li><div>Proven ability to translate business problems into analytic approaches: define hypotheses, design analyses, and synthesize results into clear recommendations.
</div></li><li><div>Strong proficiency with modern ML frameworks and cloud platforms (TensorFlow, PyTorch , Azure, AWS) &ndash; Microsoft AI Factory
</div></li><li><div>Strong technical fluency with modern analytics stacks, data modeling, SQL, and experience partnering effectively with engineering teams.<br>
</div></li><li><br>
<b>Machine Learning &amp; Advanced Analytics
</b><ul><li>Hands-on experience developing and deploying <b>machine learning models </b>, including <b>time-series forecasting, predictive modeling, and optimization use cases
</b></li><li>Strong understanding of <b>model performance, validation, stability, and business impact
</b></li></ul></li><li><b>Generative AI &amp; AI Agents
</b><ul><li>Practical experience with <b>Generative AI solutions </b>, including <b>copilots, intelligent automation, and agent-based workflows
</b></li><li>Ability to embed GenAI capabilities into enterprise processes to improve decision-making and operational efficiency
<br>
<br>
Good to Have Capabilities
</li></ul></li><li><b>Data Engineering (IT + OT)
</b><ul><li>Experience designing and maintaining <b>data pipelines </b>across IT and OT environments
</li><li>Exposure to <b>sensor data, streaming / real-time data processing </b>, and industrial data sources
</li><li>Ability to collaborate with data engineering teams to ensure reliable, timely, and trusted data flows
</li></ul></li><li><b>MLOps / AgentOps
</b><ul><li>Experience in <b>model deployment and lifecycle management </b>, including:
<ul><li>Transition from model development to <b>production and scale
</b></li><li>Monitoring, retraining, versioning, and drift management
</li></ul></li><li>Familiarity with automation and operationalization of ML/AI workloads
<br>
<br>
Preferred Experience &amp; Platforms
</li></ul></li><li><b>Cloud &amp; Analytics Platforms
</b><ul><li>Experience working with enterprise cloud platforms, preferably:
<ul><li><b>Microsoft Azure Data Platform
</b></li><li><b>Databricks AI Platform
</b></li><li><b>Microsoft AI Foundry / Microsoft AI Factory
</b></li></ul></li>Understanding of cloud-native architectures for scalable analytics and AI solutions<br>
<li>
</li></ul></li></ul><div></div><p><b>Core Competencies:
</b></p><ul><li><p><b>Model Accuracy &amp; Reliability: </b>Performance, drift stability, and operational uptime.
</p></li><li><p><b>Adoption &amp; Business Impact: </b>Value realized, user adoption, integration success.
</p></li><li><p><b>Delivery Velocity: </b>Timeliness of development cycles and deployment readiness.
</p></li><li><p><b>Compliance &amp; Quality: </b>Alignment with Responsible AI, governance, and documentation standards.</p></li></ul></div><p></p>

Principal Specialist, Data Science & Analytics

Google

<div>
 In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:
 <br/>
 <br/>
 <ul>
  <li>
   Health, dental, vision, life, disability insurance
  </li>
  <li>
   Retirement Benefits: 401(k) with company match
  </li>
  <li>
   Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
  </li>
  <li>
   Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
  </li>
  <li>
   Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
  </li>
  <li>
   Baby Bonding Leave: 18 weeks
  </li>
  <li>
   Holidays: 13 paid days per year
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Minimum qualifications:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  </li>
  <li>
   3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred qualifications:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  About the job
 </strong>
 Chrome&rsquo;s mission is to make the web work better for you. We do this by evolving Chrome, which serves the world and Google as both a product (4 billion plus clients) and a platform (that is: Chromium and related components that advance the open web and open media technologies).
 <br/>
 <br/>
 As a platform, Chrome envisions a fast, safe, capable platform and thriving web for generations of users and developers. As a product, Chrome imagines a more helpful, adaptive agent that helps people with their multifaceted needs. Chrome's goal is to redefine browsing by delivering unparalleled safety, speed, efficiency, and ease of use, and integrating with Generative Artificial Intelligence (GenAI) and Google's web products, to understand user needs and provide personalized experiences.
 <br/>
 <br/>
 In this role, your work will have tremendous impact throughout Chrome, as you will have an opportunity to work with the many teams that use our systems to gate features and understand users.You will blend research and product expertise to manage issues.
 <br/>
 <br/>
 You will leverage a mix of theoretical and practical knowledge in advanced statistical and machine learning (ML) techniques, predictive modeling, human evaluation, experimentation, forecasting, exploratory analysis, and more to drive data-informed innovation, unlock opportunities, and enhance user experiences within Chrome.The US base salary range for this full-time position is $147,000-$211,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
 <br/>
 <br/>
 Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .
 <br/>
 <br/>
 <strong>
  Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Leverage advanced statistical methods on massive, datasets to extract insights from billions of events and thousands of features across organizational sources.
  </li>
  <li>
   Analyze intricate product and platform usage patterns, translating data-driven insights into actionable product strategy and engineering decisions.
  </li>
  <li>
   Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
  </li>
  <li>
   Navigate technical or methodological conversations and narrative-telling presentations. Make clear, concise product and engineering recommendations to drive major impact. Use coding and methodology.
  </li>
  <li>
   Demonstrate an interest and aptitude in data, metrics, analysis, and trends, and applied knowledge of measurement, statistics, and program evaluation.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form .
</div>


Research Data Scientist, Chrome

Batuta Diseno

<p><b>Head of AI &amp; Machine Learning &ndash; IT &amp; Digital Transformation</b></p><p>Company: Batuta Diseno<br/>Location: Doha<br/>Salary: QAR 50,000 per month</p><p>Job Summary</p><p>Batuta Diseno is seeking an experienced and innovative Head of AI &amp; Machine Learning to lead the company&rsquo;s AI initiatives and digital transformation programs. The ideal candidate will oversee the development of intelligent systems, machine learning models, and AI-driven solutions that enhance business operations, automation, and strategic growth.</p><p>Key Responsibilities</p><ul><li>Develop and implement AI and machine learning strategies aligned with business and digital transformation goals.</li><li>Lead AI/ML teams in designing, building, testing, and deploying intelligent applications and predictive models.</li><li>Manage AI-driven automation, data analytics, natural language processing, and deep learning projects.</li><li>Collaborate with business leaders, IT teams, and stakeholders to identify AI opportunities and innovative solutions.</li><li>Oversee AI infrastructure, cloud platforms, and data engineering processes.</li><li>Ensure scalability, security, compliance, and performance of AI systems and digital platforms.</li><li>Monitor AI model accuracy, optimize performance, and improve machine learning pipelines.</li><li>Research emerging AI technologies and recommend innovative solutions to improve operations and customer experiences.</li><li>Establish AI governance, ethical standards, and best practices for AI implementation.</li><li>Provide leadership, mentoring, and technical guidance to AI engineers, developers, and data scientists.</li></ul><p>Qualifications</p><ul><li>Bachelor&rsquo;s or Master&rsquo;s degree in Artificial Intelligence, Computer Science, Data Science, Engineering, or related field.</li><li>Minimum of 8+ years of experience in AI, machine learning, data science, or digital transformation leadership roles.</li><li>Strong expertise in machine learning frameworks such as TensorFlow, PyTorch, Keras, or Scikit-learn.</li><li>Proficiency in Python, R, SQL, and big data technologies.</li><li>Experience with cloud platforms such as AWS, Azure, or Google Cloud for AI/ML deployment.</li><li>Strong understanding of predictive analytics, NLP, deep learning, and AI model optimization.</li><li>Experience leading enterprise AI and automation projects.</li><li>Excellent leadership, strategic thinking, communication, and problem-solving skills.</li><li>Ability to manage cross-functional teams and complex digital transformation initiatives.</li><li>Relevant AI, data science, or cloud certifications are preferred.</li></ul><p>الراتب المدفوع: QAR٠٫٠٠&nbsp;لكل شهر</p><p>موقع العمل: بشكل شخصي</p>

Head of AI & Machine Learning

Intuit

<div><h2 class="jobSectionHeader"><b>Overview</b></h2><p>
Intuit enables millions of small and mid-sized businesses to grow through marketing automation and customer intelligence. As we accelerate toward becoming an AI-driven platform, Product Data Science must evolve to operate at a new level of technical sophistication, scale, and strategic influence.</p><p></p><p><br>
We are seeking a Principal Data Science Leader to partner with the leadership team and lead the AI transformation of the Product Data Science organization. This leader will modernize how data science is practiced by advancing modeling capabilities, experimentation rigor, generative AI adoption, and scalable decision systems while driving measurable revenue, growth, and expert network outcomes.</p><p></p><p><br>
This role requires a proven track record of building durable data science capabilities and production-grade solutions, not just delivering analyses. The ideal candidate will elevate the craft across the organization, establish higher technical standards, and position data science as a strategic force multiplier for product, growth, and expert performance.</p><p><b><br>
Responsibilities</b></p><p>
Strategic Leadership</p><ul><li>
Define and lead the multi-year vision for Product Data Science, aligning AI, modeling, and experimentation investments with product strategy and measurable business outcomes.</li><li>
Partner with Data Science leadership to transform the function into an AI-native organization by modernizing tools, workflows, technical standards, and scalable decision systems.</li><li>
Outcome-Driven Product Impact</li><li>
Own revenue and growth accountability for data science-driven initiatives, ensuring modeling, experimentation, and AI investments are directly tied to measurable business outcomes and prioritized for maximum impact.</li><li>
Lead the rapid development and deployment of predictive models, causal inference frameworks, and LLM-enabled solutions, leveraging modern AI tools to accelerate learning velocity and reduce time from insight to decision.</li><li>
Drive a high-speed experimentation culture, balancing rigor with agility, so teams continuously test, learn, and iterate. Translate insights into scalable impact with clear post-launch accountability.</li></ul><p></p><p><br>
Expert Optimization</p><ul><li>
Build and own the data science framework for expert optimization: connecting expert skill signals, product and tool usage patterns, and downstream performance outcomes into a unified model of expert quality and capacity.</li><li>
Develop skill-to-outcome models that link expert proficiency across product domains (tool adoption, workflow adherence, knowledge depth) to measurable service quality metrics, enabling targeted coaching, routing, and capacity decisions.</li><li>
Partner with Expert Network and Operations leadership to translate expert behavioral signals (product usage patterns, interaction telemetry, knowledge assessments) into actionable intelligence. Identify which skills drive quality outcomes, which tool gaps create friction, and how to close both at scale.</li><li>
Design and scale experimentation frameworks to rigorously measure the causal impact of training interventions, tooling changes, and product investments on expert performance outcomes such as resolution quality, customer satisfaction, and proficiency scores.</li><li>
Build expert lifecycle analytics from onboarding through proficiency attainment, identifying acceleration levers and friction points that predict long-term performance and retention.</li><li>
Partner with product teams to close the loop between product and tool design and expert outcomes, surfacing where underutilization or misuse of tooling is suppressing quality and feeding those signals back into roadmap decisions.</li></ul><p></p><p><br>
Capability Building and Craft Elevation</p><ul><li>
Champion and scale durable Product Data Science capabilities by establishing rigorous standards across modeling, experimentation, model governance, and AI-enabled workflows. Raise the technical bar through structured reviews, reusable frameworks, and clear quality benchmarks.</li><li>
Accelerate adoption of modern AI tools, LLMs, and automation to increase productivity, technical depth, and organizational leverage across the Data Science team.</li><li>
Mentor, develop, and retain top data science talent by fostering a culture of ownership, technical excellence, and continuous learning.</li></ul><p></p><p><b><br>
Qualifications</b></p><ul><li>
10+ years of experience in Product Data Science with a demonstrated track record of personally building, shipping, and scaling high-impact models and experimentation frameworks that drove measurable revenue and growth outcomes.</li><li>
Proven experience transforming data science practices, establishing modeling standards, experimentation rigor, reusable frameworks, and AI-native workflows across teams.</li><li>
Deep technical expertise in SQL and Python, with strong command of statistical modeling, causal inference, experimentation design, and machine learning at production scale.</li><li>
Hands-on experience applying LLMs and modern AI tooling to accelerate insight generation, modeling workflows, and decision velocity.</li><li>
Demonstrated ability to influence senior cross-functional leaders and drive strategic outcomes without direct authority.</li><li>
Strong mentorship track record, with evidence of elevating senior data scientists and raising the technical bar of an organization.</li><li>
Master's degree or higher in Statistics, Computer Science, Economics, Applied Mathematics, Machine Learning, or a related quantitative field.</li><li>
Deep understanding of growth science, customer lifecycle value, segmentation, and personalization at scale.</li><li>
Experience productionizing AI-driven decision systems in complex product ecosystems.</li><li>
Familiarity with multi-product ecosystems and cross-sell and attach strategies within platform businesses.</li><li>
Experience building behavioral analytics or skill and performance optimization systems in marketplace, gig, or expert network contexts, connecting workforce supply-side signals to customer demand-side outcomes.</li><li>
Prior work with human-in-the-loop AI systems where expert or agent quality is a lever for product performance.</li></ul><p><br>
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit&reg;: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is:</p><div>
Bay Area California $ 247,500- 335,000</div><div>
Southern California $ 241,000- 326,500</div></div><p></p>

Principal Data Scientist - Expert Optimization, ICS Data Science

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