Top Data Science Jobs

Partner with product owners to translate business problems into scalable data science, ML, AI, and GenAI solutions. Develop predictive, forecasting, optimization, and simulation models; analyze large datasets; build dashboards; deploy models in cloud environments; and contribute to data governance and infrastructure improvements.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Own end-to-end data science for emerging content surfaces, including product experimentation, causal inference, exploratory analysis, metrics, dashboards, and strategic insights. Identify opportunities to grow content supply, creators, and engagement while improving discovery. Lead content data architecture and quality initiatives, develop source-of-truth assets, and apply advanced statistical methods such as quasi-experiments and network testing. Partner with Product, Engineering, Design, and Research to influence product direction and communicate findings to diverse audiences.

Leads development and deployment of machine learning and graph analytics solutions to detect identity theft, account takeover, synthetic fraud, scams, disputes, and AML risks. Partners with business and technology teams on graph strategy, data integration, model governance, and production deployment. Develops reusable algorithms, communicates technical insights to executives, manages project risks, and mentors junior data scientists.

Analyze complex structured and unstructured datasets to answer client questions, evaluate AI and machine learning models, and deliver objective insights for leadership decisions. Collaborate with clients and teammates, mentor colleagues, and strengthen data science capabilities supporting Department of War or Intelligence Community clients. The role may involve GEOINT, LLMs, and all-source analysis in a classified environment.

Develop descriptive, predictive, and causal analytics for local government datasets. Responsibilities include maintaining data pipelines, cleaning and refreshing databases, weighting surveys, building statistical and machine learning models, conducting impact studies, automating cloud workflows, presenting insights, and documenting reproducible processes. The role partners with engineering, marketing, sales, customer success, and survey research teams, and requires Python, SQL, statistics, communication, and applied data science experience.

Analyze and clean data, develop statistical and predictive models, conduct A/B testing, identify business opportunities, and communicate insights to stakeholders. The role also involves evaluating data sources, creating tailored algorithms, implementing analytics, and monitoring model performance and data accuracy.

Partner with Product, Engineering, and Go-To-Market teams to define success metrics, design experiments, evaluate product launches, analyze large-scale ad-tech data, and guide roadmap decisions. The role requires translating complex data science findings into actionable recommendations for technical and non-technical stakeholders while applying rigorous statistical, modeling, and experimentation practices.

Develop algorithms, predictive models, quantitative analyses, and visualizations from structured and unstructured data. Perform data exploration, cleaning, analysis, mining, and visualization to help clients make informed decisions. Collaborate with clients to understand business needs and transform complex data into actionable insights. The role supports applications involving IoT, machine learning, artificial intelligence, fraud detection, cancer research, and national intelligence.

Develops AI and machine learning healthcare products, including NLP models for unstructured medical records and classification models for clinical programs. Responsibilities include designing, training, deploying, and evaluating ML and deep learning architectures; conducting statistical analysis, exploratory data analysis, and visualization; resolving data issues; delivering analytic insights; and supporting governance, reporting, and process improvements.

Develop and maintain large housing datasets and infrastructure for US housing policy research. Responsibilities include data acquisition, cleaning, analysis, modeling, automation, visualization, dashboard development, web scraping, API use, quality control, and publication. Senior responsibilities add predictive modeling, machine learning, workflow supervision, project management, and establishing data-management best practices.

Build production data pipelines, reliability models, scoring systems, analytics tools, APIs, and LLM-powered workflows for Starlink hardware reliability. Analyze telemetry, manufacturing, inventory, field-return, support, and image data; deploy and monitor machine learning models; and integrate outputs into replacement, support, production, quality, and supply-chain decisions. The role requires cross-functional collaboration and on-site work in Bastrop, Texas, with up to 10% travel and occasional extended hours.

Build and deploy machine learning, NLP, and LLM-powered solutions at scale. Improve production model performance, reliability, and accuracy; develop datasets, metrics, tools, and experimentation frameworks; and use data to influence product strategy. Partner with Product, Engineering, and business stakeholders to drive AI innovation, while serving as a technical thought leader and mentoring other data scientists.

Own the design and build of a data-quality and assurance function for a large-scale pricing intelligence data lake. Responsibilities include architecting observability, defining quality standards, building anomaly detection and health scoring, instrumenting pipelines, answering customer data-quality questions, and tracing issues to root causes across collection, processing, and source changes.

Develop and implement AI, machine learning, analytics, automation, and data solutions for Corporate and Investment Banking Operations and Controls. Partner with business and technology stakeholders to identify and prioritize use cases, build and test scalable solutions, apply governance controls and guardrails, monitor performance, and demonstrate ROI. The role also supports AI adoption, stakeholder training, governance compliance, and best-practice development across the organization.

Develop and implement AI and machine learning solutions for Corporate and Investment Banking Operations and Controls. Responsibilities include identifying and prioritizing use cases, building data products and models, collaborating with IT and business stakeholders, applying AI governance and risk controls, testing and monitoring solutions, creating documentation and training materials, and tracking KPIs, ROI, accuracy, and performance. The role also promotes AI best practices and adoption across the organization.

Designs, builds, and analyzes complex structured and unstructured datasets; applies statistical methods, visualization, feature engineering, and machine learning to solve business problems. Develops revenue-generating models, identifies cost savings, optimizes processes, and delivers business insights using cloud, big data, data engineering, MLOps, and advanced machine learning tools.

Lead end-to-end AI and data science initiatives across Alcoa’s Operations and Enterprise functions. Develop, validate, deploy, and continuously improve statistical, machine learning, generative AI, RAG, and agentic AI solutions. Translate business challenges into scalable models, evaluate outcomes, and deliver measurable value. Provide technical leadership, mentor data scientists, and collaborate with engineers, architects, analysts, and stakeholders to build trusted, responsible AI capabilities.

Build and validate predictive models, analyze large structured and unstructured datasets, generate business insights, present analytic results to stakeholders, and develop tools to advance analytics capabilities.

The Senior Associate, Data Scientist will partner with cross-functional teams to identify risks associated with models and validate machine learning models across the enterprise, enhancing decision-making while ensuring model governance and risk management.

The Data Scientist develops advanced analytics, AI, machine learning, predictive models, and business intelligence solutions supporting pharmaceutical commercial strategies. Responsibilities include analyzing complex healthcare and longitudinal patient data, building scalable algorithms and automated analytics capabilities, integrating and governing cloud data, and communicating insights to technical and non-technical stakeholders. The role partners cross-functionally to support market intelligence, customer segmentation, targeting, promotional measurement, and data-driven decision-making across oncology, rare disease, and CNS franchises.

Build and deploy data science models for demand and sales forecasting, promotion effectiveness, pricing, and retail analytics. Analyze messy datasets from sell-through, inventory, deductions, trade promotions, and supply-chain signals. Develop statistical quality checks, evaluate model performance, quantify uncertainty, and collaborate with AI/ML engineers to ship production-grade insights within the product.

Leverage data science, machine learning, statistical analysis, and big-data techniques to solve business problems and support commercial strategy. Develop, deploy, and measure models, algorithms, automation, and analytical solutions. Collaborate with BI and enterprise data teams to improve reporting, visualizations, and data models. Present insights and recommendations to varied audiences while tracking business impact and evaluating emerging data engineering technologies.

Develop predictive models, machine learning algorithms, quantitative analyses, and visualizations using structured and unstructured data. Explore, clean, analyze, mine, and interpret complex datasets while collaborating with clients to address data-driven challenges. Lead solution development, communicate findings, and support informed decision-making across public and private sector projects. Travel up to 25% and maintain a Secret clearance.

Develop predictive and machine learning models, establish data strategies, source and validate data, create visualizations, and deploy scalable production solutions. Translate complex business requirements into actionable insights supporting Walmart’s e-commerce search backend. Monitor model performance, document code, collaborate cross-functionally, and guide junior team members while applying statistical analysis, NLP, and large language model expertise.