statistical modeler

Transpo Group Middle East

Hiring: Financial Modeler | Industrial / Manufacturing ProjectsWe are currently seeking an experienced Financial Modeler to support a major industrial project (on bid stage). This role is ideal for candidates with a strong background in financial analysis, project modeling, and manufacturing operations, particularly within the metal production sector.Please note: This opportunity is tied to a project under tender/bid stage. Shortlisted candidates will be considered for inclusion in the proposal and mobilization upon award.Key Responsibilities<ul><li>Develop and maintain complex financial models for large-scale industrial/manufacturing projects</li><li>Conduct feasibility studies, financial forecasting, and scenario analysis</li><li>Support investment appraisals, budgeting, and cost analysis</li><li>Collaborate with engineering, commercial, and project teams to align financial assumptions with technical inputs</li><li>Analyze project risks, returns, and funding structures</li><li>Prepare reports and presentations for senior stakeholders and investors</li></ul>Requirements<ul><li>Bachelor&rsquo;s degree in Accountancy, Finance, or related field</li><li>Minimum 10 years of experience in financial modeling and analysis</li><li>At least 5 years of experience within industrial or manufacturing sectors</li><li>Strong expertise in project finance, financial forecasting, and valuation models</li><li>Advanced proficiency in Excel and financial modeling tools</li><li>Excellent analytical, communication, and stakeholder management skills</li></ul>Preferred Qualifications<ul><li>Experience in metal production, mining, or heavy industrial projects</li><li>Exposure to EPC / PMC environments or large-scale capital projects</li><li>Professional certifications (CPA, CFA, or equivalent) are an advantage</li></ul>Interested?If you meet the above criteria and are open to being considered for this opportunity, please share your updated CV.Kindly note that inclusion in the proposal does not constitute a formal offer but positions you for priority consideration upon project award.Note to Applicants: Please read the job requirements carefully. If you do not have the required experience especially in the industrial sector, please do not apply.Application Question(s):<ul><li>Did you include the project experience in your CV?</li><li>Is your most recent CV attached to your application?</li></ul>Education:<ul><li>Bachelor's (Preferred)</li></ul>Experience:<ul><li>financial modeling: 5 years (Required)</li><li>construction industry: 5 years (Required)</li><li>industrial/manufacturing project: 5 years (Required)</li></ul>Work Location: In person

Finance Modeler (Bid Stage)

Oman Air

<div>
 
 
 Job Purpose
 
 
 
 The Financial Modeler &ndash; Strategy &amp; Analytics will support the development of financial models and analytical tools to enable data-driven decision-making across Oman Air. The role combines financial modeling with data analytics to support route profitability, network optimization, and investment analysis.
 
 
 The position contributes to enhancing financial visibility and supports business cases, forecasting, and performance analysis across commercial and strategic initiatives.
 
 
 
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 
 Financial Modeling &amp; Forecasting
 
 </li>
 </ul>
 
 - Support development and maintenance of financial models for planning and forecasting.
 
 
 - Assist in building models linking revenue, cost, and operational drivers.
 
 
 - Contribute to scenario analysis and financial projections.
 
 
 
 
 <ul>
 <li>
 
 Route Profitability &amp; Network Analytics
 
 </li>
 </ul>
 
 - Support development of route profitability models (RASK, CASK, contribution).
 
 
 - Assist in analyzing route performance and identifying optimization opportunities.
 
 
 - Provide analytical support to network planning and commercial teams.
 
 
 
 
 <ul>
 <li>
 
 Data Analytics &amp; Integration
 
 </li>
 </ul>
 
 - Utilize Python, SQL, and Excel to analyze datasets and improve insights.
 
 
 - Support integration of financial and operational data into models.
 
 
 - Assist in developing dashboards and automated tools.
 
 
 
 
 <ul>
 <li>
 Business Case Support
 </li>
 </ul>
 
 - Assist in building financial models for new routes, investments, and initiatives.
 
 
 - Support NPV, IRR, and sensitivity analysis.
 
 
 - Contribute to preparation of investment materials.
 
 
 
 
 <ul>
 <li>
 
 Performance Analysis
 
 </li>
 </ul>
 
 - Analyze financial performance against budget and forecasts.
 
 
 - Support development of KPIs and performance frameworks.
 
 
 - Link operational metrics with financial outcomes.
 
 
 
 
 <ul>
 <li>
 
 Process Improvement
 
 </li>
 </ul>
 
 - Support improvement and automation of modeling tools.
 
 
 - Assist in standardizing templates and methodologies.
 
 
 - Enhance data accuracy and reporting efficiency.
 
 
 
 
 <ul>
 <li>
 
 Stakeholder Collaboration
 
 </li>
 </ul>
 
 - Work with Commercial, Network, Finance, and Strategy teams.
 
 
 - Translate analysis into clear insights for decision-making.
 
 
 - Support senior team with analytical inputs.
 
 
 
 
 
 
 Qualifications
 
 
 
 - Bachelor&rsquo;s degree in Computer Science, Finance, Economics, Engineering, or related quantitative field.
 
 
 - CFA Candidate (Level III / Finalist) preferred.
 
 
 
 
 
 
 Experience
 
 
 
 - 1&ndash;3 years of experience in analytics, finance, or technology-related roles.
 
 
 - Exposure to financial modeling or investment analysis is desirable.
 
 
 - Experience with data tools (Python, SQL) is an advantage.
 
 
 
 
 
 
 Skills &amp; Competencies
 
 
 
 - Strong Excel skills (financial modeling foundation).
 
 
 - Basic to intermediate Python and/or SQL.
 
 
 - Understanding of financial concepts and analysis.
 
 
 - Strong analytical and problem-solving skills.
 
 
 - Ability to communicate insights clearly.
 
 
 
 
 
 
 Personal Attributes
 
 
 
 - Analytical and detail-oriented.
 
 
 - Strong learning mindset.
 
 
 - Collaborative and proactive.
 
 
 - Ability to work under guidance and grow into the role.
 
</div>

Financial Modeler – Strategy & Analytics

Attune

<div>
 
 Position Summary
 
 
 
 The Data Scientist is responsible for applying advanced analytics, statistical modeling, and machine learning techniques to support strategic decision-making across the organization. This role partners closely with stakeholders in Underwriting, Actuarial, Claims, Product, Revenue, and Customer Experience to translate business questions into analytical solutions that improve operational performance, risk evaluation, and customer outcomes.
 
 
 The Data Scientist develops predictive models, explores new data sources, and generates actionable insights that support pricing, underwriting, claims management, and operational efficiency. This role also contributes to the development and operationalization of models and analytics solutions by working with engineering and product teams to integrate insights into business tools, dashboards, and workflows.
 
 
 
 Essential Duties and Responsibilities
 
 
 
 <ul>
 <li>
 Partner with stakeholders across Underwriting, Actuarial, Claims, Product, and Revenue to identify opportunities where data can inform better business decisions.
 </li>
 <li>
 Translate business questions into analytical frameworks, hypotheses, and statistical approaches.
 </li>
 <li>
 Analyze customer, broker, and operational data to identify trends, anomalies, and opportunities to improve engagement, retention, and operational performance.
 </li>
 <li>
 Conduct exploratory data analysis to investigate business problems and generate actionable insights.
 </li>
 <li>
 Communicate analytical findings through visualizations, dashboards, and written summaries.
 </li>
 <li>
 Develop predictive models and statistical analyses to support pricing, underwriting, claims, reserving, and operational initiatives.
 </li>
 <li>
 Apply machine learning techniques including regression, classification, clustering, and other statistical learning methods to solve complex business problems.
 </li>
 <li>
 Evaluate model performance and continuously improve models based on new data and changing business needs.
 </li>
 <li>
 Explore internal and third-party data sources to enhance model performance and uncover new insights.
 </li>
 <li>
 Collaborate with engineering and product teams to deploy analytical models into production systems and operational workflows.
 </li>
 <li>
 Support the implementation of MLOps practices, including model versioning, deployment pipelines, monitoring, and lifecycle management.
 </li>
 <li>
 Monitor models for performance degradation, bias, or drift and implement improvements as needed.
 </li>
 <li>
 Document model assumptions, methodology, and validation processes to support transparency and governance.
 </li>
 <li>
 Contribute to the improvement of internal data infrastructure, ETL pipelines, and reporting architecture.
 </li>
 <li>
 Support the development and optimization of dashboards and analytics tools used by business teams.
 </li>
 <li>
 Ensure data quality, consistency, and reliability across analytical workflows.
 </li>
 <li>
 Identify opportunities to automate recurring analyses and reporting processes.
 </li>
 <li>
 Work closely with business stakeholders to ensure analytics solutions align with operational needs and strategic objectives.
 </li>
 <li>
 Partner with product and engineering teams to integrate analytical capabilities into internal platforms and decision tools.
 </li>
 <li>
 Provide analytical support for ad-hoc requests and strategic initiatives.
 </li>
 <li>
 Other duties as assigned.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 
 Education &amp; Experience
 
 
 
 <ul>
 <li>
 Bachelor's degree in data science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative field required.
 </li>
 <li>
 Advanced degree (MS or PhD) preferred.
 </li>
 <li>
 3 - 7 years of experience in data science, advanced analytics, or statistical modeling roles.
 </li>
 <li>
 Experience working with large datasets and complex analytical problems.
 
 
 </li>
 </ul>
 
 Knowledge, Skills, and Abilities
 
 
 
 <ul>
 <li>
 Strong programming skills in Python and SQL.
 </li>
 <li>
 Experience with statistical modeling and machine learning techniques.
 </li>
 <li>
 Experience with data visualization and analytical tools (e.g., Tableau, Power BI, Looker, or similar).
 </li>
 <li>
 Ability to translate complex analytical findings into clear business insights.
 </li>
 <li>
 Strong problem-solving and critical thinking skills.
 </li>
 <li>
 Ability to collaborate effectively with both technical and non-technical stakeholders.
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience working with Property &amp; Casualty (P&amp;C) insurance data, including underwriting, claims, or pricing analytics.
 </li>
 <li>
 Familiarity with advanced statistical techniques such as Bayesian modeling or hierarchical models.
 </li>
 <li>
 Experience with MLOps frameworks, model deployment pipelines, and model monitoring tools.
 </li>
 <li>
 Experience working in cloud data environments (AWS, GCP, Azure).
 </li>
 <li>
 Familiarity with data engineering concepts, including ETL pipelines and data warehouse architectures.
 </li>
 <li>
 Experience conducting experiments or A/B testing to evaluate business initiatives.
 
 
 </li>
 </ul>
 
 Physical Requirements
 
 
 
 This position primarily operates in a professional office or remote environment and routinely uses standard office equipment such as computers and phones. The ability to remain stationary for extended periods and operate a computer is required.
 
 
 
 What We Offer:
 
 
 
 <ul>
 <li>
 Flexible PTO
 </li>
 <li>
 Generous parental and caregiver leave
 </li>
 <li>
 401K match
 </li>
 <li>
 Excellent medical, dental, and vision plans
 </li>
 <li>
 Remote-first culture
 </li>
 <li>
 Annual $1000 tuition reimbursement stipend
 </li>
 <li>
 And more!
 
 
 </li>
 </ul>
 The expected annual salary for this position is between $120,000 and $150,000, with the opportunity to earn an annual bonus of up to 15% of the base salary.
</div>

Data Scientist

SoFi

<div>
 
 Employee Applicant Privacy Notice
 
 
 
 
 Who we are:
 
 
 
 Shape a brighter financial future with us.
 
 
 Together with our members, we&rsquo;re changing the way people think about and interact with personal finance.
 
 
 We&rsquo;re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we&rsquo;re at the forefront. We&rsquo;re proud to come to work every day knowing that what we do has a direct impact on people&rsquo;s lives, with our core values guiding us every step of the way.
 
 Join us to invest in yourself, your career, and the financial world.
 
 
 
 
 The Role
 
 
 
 The Risk Data Science team is looking for a Data Scientist/Senior Data Scientist to develop advanced machine learning and statistical models, guide measurement, strategy, and data-driven decision making to support various credit risk and operational areas at SoFi. The Data Scientist will work closely with Credit, Risk, Product, Engineering, and Operations teams to design solutions for underwriting, portfolio management, loss mitigation, and loss forecasting etc. These tasks involve researching and applying state of the art modeling methodologies to solve complex business problems. This role is very rewarding as your work will have a direct and immediate impact on the business&rsquo; profitability.
 
 
 
 What You&rsquo;ll Do
 
 
 
 
 <ul>
 <li>
 Develop, implement, and continuously improve machine learning and statistical models that support various credit, risk, and operational procedures including but not limited to underwriting, portfolio management, loss mitigation, and loss forecasting, etc.
 </li>
 <li>
 Present model performance and insights to Credit, Risk, and Business Unit leaders.
 </li>
 <li>
 Proactively identify opportunities to apply advanced modeling approaches to solve complex business problems.
 </li>
 <li>
 Explore and leverage in-house and external data sources to enhance model predictive power.
 </li>
 <li>
 Collaborate with the Model Risk Management team to demonstrate models are developed with high level rigor that satisfy Model Risk Management and Governance requirements.
 </li>
 <li>
 Perform ongoing monitoring of the models through the construction of dashboards and KPI tracking
 </li>
 <li>
 Collaborate with the Product and Engineering teams to improve the model development, deployment, monitoring, and model re-calibration/re-build process..
 </li>
 <li>
 Explore and apply in-house and open-source machine learning and statistical tools and algorithms to develop and improve models.
 
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Need
 
 
 
 
 <ul>
 <li>
 Master&rsquo;s degree in Statistics, Econometrics, Mathematics, Operations Research, Physics, Computer Science, Engineering, or quantitative field required. PhD degree preferred.
 </li>
 <li>
 2+ years of relevant work experience in building and implementing machine learning and statistical models.
 </li>
 <li>
 Excellent logic reasoning and communication abilities when interpreting business requirements and translating them into effective data solutions.
 </li>
 <li>
 Strong skills in writing efficient SQL queries and Python code to create complex attributes, especially with large datasets.
 </li>
 <li>
 Strong sensitivity to details in data and proactively investigate them to uncover unknown patterns.
 </li>
 <li>
 Strong knowledge of databases and related languages/tools such as SQL, NoSQL, Hive, etc.
 </li>
 <li>
 Demonstrated sophisticated experience in building efficient and reliable pipelines that interact with large datasets stored in SageMaker and Snowflake, automating recurring processes such as data extraction and processing, feature selection, model training, model monitoring, and generating documentation templates to support reproducibility and cross-functional collaboration.
 </li>
 <li>
 Excellent knowledge of machine learning and statistical modeling methods for supervised and unsupervised learning. These methods include (but are not limited to) regression, classification, clustering, outlier detection, novelty detection, decision trees, nearest neighbors, support vector machines, ensemble methods and boosting, neural networks, deep learning and its various applications. Continuously following the advancement of machine learning and artificial intelligence to update your knowledge and skills in order to solve business problems with the most efficient methodologies
 </li>
 <li>
 Strong programming skills in Python and machine learning libraries (e.g., sklearn, lightgbm, xgboost, pytorch, tensorflow, keras, etc.)
 
 
 
 </li>
 </ul>
 
 Nice To Have
 
 
 
 
 <ul>
 <li>
 Experience in a lending organization.
 </li>
 <li>
 Experience with model documentation and delivering effective verbal and written communication.
 </li>
 <li>
 Experience in working closely with Product, Engineering, and Model Risk Management teams.
 </li>
 <li>
 Experience with AWS or GCP.
 
 
 
 </li>
 </ul>
 
 Compensation And Benefits
 
 
 
 The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate&rsquo;s experience, skills, and location.
 
 
 To view all of our comprehensive and competitive benefits, visit our
 
 Benefits at SoFi
 
 page!
 
 
 SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law.
 
 
 The Company hires the best qualified candidate for the job, without regard to protected characteristics.
 
 
 Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
 
 
 New York applicants: Notice of Employee Rights
 
 
 SoFi is committed to an inclusive culture. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email accommodations@sofi.com.
 
 
 Due to insurance coverage issues, we are unable to accommodate remote work from Hawaii or Alaska at this time.
 
 
 
 Internal Employees
 
 
 
 If you are a current employee, do not apply here - please navigate to our Internal Job Board in Greenhouse to apply to our open roles.
</div>

Google

<div><h3 class="jobSectionHeader">Minimum qualifications:</h3><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.</li></ul> 
<h3 class="jobSectionHeader">
Preferred qualifications:</h3><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.</li>
<li>Experience designing and building statistical models or physical models for energy consumption.</li>
<li>Experience in energy or climate domains.</li>
<li>Experience designing and building optimization, simulation, and probabilistic decision-making models.</li>
<li>Excellent problem-framing, problem-solving and project management skills.</li>
</ul><div><h2 class="jobSectionHeader">About the job</h2>
In this role, you will lead data modeling and forecasting efforts for our AI/ML software footprint. This includes collaborating with internal teams to gather, analyze, and interpret data related to AI/ML software usage, resource consumption, energy consumption, and carbon emissions. You will develop modeling methodologies that accurately estimate the carbon emissions of AI/ML software. You will help identify key factors, such as model complexity, training data volumes, and deployment patterns, to identify and highlight opportunities for emissions reductions. You will also develop metrics to evaluate and prioritize AI/ML emissions reductions projects.
You will work closely with the Advanced Energy Labs team, helping to close Google&rsquo;s most critical energy gaps by driving internal and external data and software products from 0 to 1. You will also work closely with the Climate Operations team, be responsible for ensuring that Google achieves Net Zero emissions and 24/7 carbon-free energy by 2030 for our global operations and value chain.
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.<div></div><div> 
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.</div></div><div><h2 class="jobSectionHeader">
Responsibilities</h2><ul>
<li>Collaborate with cross-functional stakeholder to understand their business needs and frame investigative problems. 
</li>
<li>Develop, maintain, support, and enhance models and estimation tools for the energy-use and carbon-footprint of Google Cloud customer workloads. 
</li>
<li>Drive analysis and modeling, drawing from multiple methods and choosing the right method and level of complexity appropriate for the business challenges. 
</li>
<li>Collaborate with subject matter experts to combine hardware insights with energy and emissions data to prioritize carbon-reduction efforts in Cloud. 
</li>
<li>Identify, prioritize, frame, and structure complex and ambiguous challenges, where advanced analytics projects or tools can have the biggest impact. 
</li>
</ul></div>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>

Data Scientist, Research, Operations and Infrastructure Data Science

Highbrow LLC

<div>
 
 
 Job Description
 
 
 
 
 <ul>
 <li>
 Around 10+ years of advanced hands-on experience in data science,
 
 statistical modeling, and analytics using
 
 
 Python and R
 
 </li>
 <li>
 
 Strong SQL skills
 
 , including complex joins, aggregations, window functions, sorting, and query optimization
 </li>
 <li>
 Proven experience working with large-scale structured and unstructured datasets across flat files, relational databases, cloud platforms, and distributed systems
 </li>
 <li>
 
 Strong exposure to GCP and Microsoft Azure and cloud-based analytics/data science environments
 
 </li>
 <li>
 Experience with
 
 Spark, Databricks, and large-scale data processing frameworks
 
 </li>
 <li>
 Experience with analytics and data science tools such as
 
 Dataiku and RapidMiner
 
 </li>
 <li>
 Solid understanding of descriptive statistics, hypothesis testing, EDA, and feature analysis
 </li>
 <li>
 Experience in telecom or similarly complex, multi-domain environments preferred
 </li>
 <li>
 Strong knowledge and hands-on experience with supervised and unsupervised machine learning methods, including:
 </li>
 <li>
 o
 
 Linear and logistic regression
 
 </li>
 <li>
 o Decision trees and tree-based methods
 </li>
 <li>
 o Random forest, gradient boosting, and other ensemble techniques
 </li>
 <li>
 o Support Vector Machines
 </li>
 <li>
 o Clustering methods such as k-means, hierarchical clustering, and DBSCAN
 </li>
 <li>
 o Dimensionality reduction techniques such as PCA
 </li>
 <li>
 Experience building predictive and classification models for business use cases such as:
 </li>
 <li>
 o Customer churn prediction
 </li>
 <li>
 o Customer segmentation
 </li>
 <li>
 o Revenue forecasting
 </li>
 <li>
 o Campaign response and propensity modeling
 </li>
 <li>
 o Anomaly and fraud detection
 </li>
 <li>
 o Service performance and network issue prediction
 </li>
 <li>
 o Customer experience and support interaction analytics
 </li>
 <li>
 Experience with time series analysis and forecasting for operational and business trend analysis
 </li>
 <li>
 Experience with feature engineering, model validation, hyperparameter tuning, and model performance evaluation
 </li>
 <li>
 Strong understanding of model evaluation metrics for regression, classification, and clustering use cases
 </li>
 <li>
 Ability to identify the appropriate modeling approach based on business problem, data quality, and operational constraints
 </li>
 <li>
 Experience supporting enterprise data environments spanning multiple business functions
 </li>
 <li>
 Knowledge of telecom KPIs, subscriber behavior, billing data, network performance data, and customer interaction datasets
 </li>
 <li>
 Familiarity with MLOps concepts, model monitoring, and model lifecycle management
 </li>
 <li>
 Experience with dashboarding and data visualization tools to present analytical findings effectively
 </li>
 <li>
 Familiarity with A/B testing, causal inference, and experimentation frameworks is a plus
 </li>
 <li>
 Experience with NLP/text analytics for customer care notes, tickets, surveys, or interaction data is a plus
 </li>
 <li>
 Exposure to recommendation systems, optimization methods, or graph/network analytics is a plus
 </li>
 <li>
 Strong people skills, team orientation, and professional attitude
 </li>
 <li>
 Excellent written and verbal communication skills, with the ability to explain complex technical concepts to business stakeholders
 </li>
 </ul>
 
 
 
 
 
 
 Job Responsibilities
 
 
 
 
 <ul>
 <li>
 Apply advanced data science and machine learning techniques to large telecom datasets to identify patterns, trends, and opportunities that improve mission and business decisions
 </li>
 <li>
 Partner with stakeholders across marketing, network, IT, billing, customer care, and other business units to understand data challenges and translate them into analytical and modeling solutions
 </li>
 <li>
 Develop, validate, and deploy statistical and machine learning models to support cross-functional operational and strategic initiatives
 </li>
 <li>
 Analyze enterprise data from multiple source systems and domains to uncover actionable insights, business drivers, operational risks, and performance opportunities
 </li>
 <li>
 Build predictive, segmentation, forecasting, and anomaly detection models relevant to enterprise and telecom use cases
 </li>
 <li>
 Perform data mining, exploratory data analysis, feature selection, and model diagnostics on large and complex datasets
 </li>
 <li>
 Work with structured, semi-structured, and distributed data environments using modern cloud and big data platforms
 </li>
 <li>
 Collaborate with data engineers, architects, analysts, and business partners to productionize models and support scalable analytical solutions
 </li>
 <li>
 Communicate findings, modeling approaches, assumptions, and recommendations clearly to both technical and non-technical audiences
 </li>
 <li>
 Contribute to best practices in data science, model governance, documentation, reproducibility, and analytical standards within the IT organization
 </li>
 </ul>
 <li>
 </li>
 
 
</div>

Amazon.com

<div><h2 class="jobSectionHeader">DESCRIPTION</h2><div>
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. 
 
The Data, Insights, Science and Optimization, Consumer Product and Tech (DISCO CPT) team is looking for a Data Scientist to join a team of Data Scientists, Analytics Leads, Business Intelligence Engineers and Data Engineers who analyze big data, provide analytics and insights as well as build models and algorithms that power product for personalized experiences. The team focuses on accelerating Amazon Music customer growth by empowering Product teams to make sound, customer-centric decisions through data and insights. We build data pipelines, science models, self-service analytics, insights and dashboards, deliver engagement and retention at scale. In this role, you will pioneer and execute advanced analytics and experimentation efforts for the Amazon Music product team. You will work with stakeholders and partners to deliver experimentation insights, author scientific artifacts, build scalable models to advance our analytics and science products. The ideal candidate must be willing to effectively lead science solutions, prioritize across multiple stakeholders and projects and be ready to jump into a fast-paced, dynamic and fun environment. 
 
Key job responsibilities 
<ul><li>Data Scientist II on this role will be a thought partner to Product and Engineering organizations through their expertise in experimentation, statistical modeling and advanced analytics</li><li>Conceptualize and drive strategic growth areas: retention, voice + visual engagement, experimentation, Gen AI initiatives, and product excellence.</li><li>Bridge science, analytics, and engineering to deliver solutions informing strategic decisions on AM technology investments and long-term business performance</li><li>Drive experimentation, measurement efficiency tooling, and ROI tracking for AM leadership</li><li>Build and maintain causal models to understand customer sentiment and its impact on key engagement/retention metrics</li><li>Develop structural and predictive models using data science workflows to deliver measurable customer results</li></ul> 
A day in the life 
<ul><li>Lead strategic initiatives across visual and voice experiences, experimentation and customer retention stakeholders</li><li>Conduct experimentation, build causal models to better understand key drivers for customer retention
</li><li>Evaluate existing metrics and lead deep dives to ensure the right measurement framework is leveraged to measure and enhance user adoption through visual and voice interfaces</li><li>Partner with product managers to translate business questions into actionable science projects</li><li>Influence product roadmaps through data-driven insights on customer behavior patterns</li><li>Identify and resolve customer experience friction points with cross-functional teams</li><li>Drive adoption of best practices and innovative solutions across the organization</li></ul></div><h2 class="jobSectionHeader">
BASIC QUALIFICATIONS</h2><ul><li>Bachelor's degree</li><li>2+ years of data scientist experience</li><li>3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience</li><li>3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience</li><li>Experience applying theoretical models in an applied environment</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>Knowledge of machine learning concepts and their application to reasoning and problem-solving</li><li>Experience in Python, Perl, or another scripting language</li><li>Experience in defining and creating benchmarks for assessing GenAI model performance</li><li>Experience applying quantitative analysis to solve business problems and making data-driven business decisions</li></ul> 
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. 
 
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company&rsquo;s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. 
 
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. 
 
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner. 
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life &amp; AD&amp;D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits. 
USA, CA, San Francisco - 157,300.00 - 212,800.00 USD annually 
USA, WA, Seattle - 136,000.00 - 184,000.00 USD annually</div>

Data Scientist, Amazon Music - DISCO

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Data Scientist, Amazon Music - DISCO