Top Data Science Jobs

Lead development and operationalization of data-driven financial forecasting (time-series, AI/ML, causal, Monte Carlo). Integrate statistical outputs into Anaplan planning, establish model governance/validation, build templates/dashboards, collaborate with FP&A and Data/IT, and create a Center of Excellence for advanced forecasting and repeatable playbooks.

Lead design and implementation of end-to-end data science architectures for multi‑INT geospatial intelligence. Build production Python pipelines, apply ML, network analytics, and advanced spatial techniques, create interactive visualizations and dashboards, mentor team members, liaise with stakeholders, and produce mission-ready intelligence products adhering to IC standards.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

Analyze large company datasets to generate business insights for product, sales, marketing and leadership. Build, validate and deploy predictive models and algorithms, design A/B testing frameworks, assess new data sources, create data architectures, and coordinate with cross-functional teams to implement and monitor model performance and outcomes.

The Staff Data Scientist will own the analytical strategy for Firefox, driving data-informed decisions through measurement frameworks, experiments, and user growth analysis, while mentoring other data scientists.

Support data analysis, prepare data for exploratory analysis, contribute to model building and validation, assist metadata and ETL tasks, and create visualizations to communicate model insights.

Lead end-to-end data science initiatives including decisioning and pricing optimization. Analyze large-scale data, build and evaluate machine learning and statistical models, and translate insights into business P&L actions. Collaborate with engineers and stakeholders, mentor peers, and provide technical and strategic leadership across the enterprise.

Lead pricing analytics as the first dedicated data scientist on the Pricing team. Design analyses, causal models, experiments, and decision frameworks linking usage, conversion, retention, and revenue to guide pricing, packaging, discounts, and monetization across consumer, SMB, and enterprise segments. Communicate executive-ready recommendations and build a repeatable pricing data science function.

Design and evaluate causal inference experiments and causal ML models to measure product impact, optimize resource allocation, and inform support operations. Build, deploy, and iterate statistical/ML models, analyze tradeoffs, collaborate cross-functionally, and communicate insights to influence decisions and scale data science capabilities.

As a Senior Data Scientist, you will develop and deploy machine-learning models, collaborating with teams to enhance data systems' performance and quality.

Work on-site in a SCIF to design and implement data science solutions for DoD cyber risk management. Lead development of algorithms, build ETL pipelines, structure unstructured data, draft analytics documentation and use cases, mentor junior analysts, and communicate findings to technical and executive stakeholders.

Design and implement analytic methods to extract meaning from large, messy NSA/CSS datasets. Build and evaluate models, manage and curate data, translate mission needs into technical requirements, and communicate technical conclusions to non-technical stakeholders while accounting for collection and processing constraints.

Lead marketplace measurement, experimentation, and causal analysis; build pricing, recommendation, and retention models; operationalize models with engineering; maintain dashboards and monitor supply-demand balance to guide product and growth decisions.

The Senior Data Scientist leads data science initiatives by leveraging mortgage industry insights, developing machine learning models, and ensuring compliance with regulations while mentoring junior team members.

The Senior Data Scientist will leverage advanced analytics, machine learning, and AI to develop analytics engines and collaborate with teams to enhance business value and communication of results.

Lead development and deployment of predictive, risk-scoring, and optimization models for behavioral health using claims and clinical data. Define data science infrastructure, perform deep analytics, translate models into client-facing impact and dashboards, and lead cross-functional product and client engagements.

SOSi is seeking a Data Scientist to develop AI/ML models, handle petabyte-scale data, and utilize Python and SQL to automate national security tasks.

The Data Scientist will build machine learning models to enhance claim audits, optimize performance, and improve healthcare decision-making by analyzing complex data sets.

Partner with Product and Engineering to define product metrics, design experiments and causal analyses, build reporting and models, analyze user behavior and segments, and translate insights into product recommendations while establishing Product Data Science practices.

The role involves building the marketing data science function, developing metrics, dashboards, and models to analyze marketing effectiveness, and fostering a data-informed culture.

Lead healthcare-focused data science projects from concept to deployment. Develop and deploy advanced ML, NLP, and generative AI models using Python and GCP (Vertex AI, BigQuery, Dataflow). Collaborate with clinicians, present findings to stakeholders, mentor junior data scientists, and ensure team follows AI/data science best practices.

The Data Scientist will analyze complex datasets, develop predictive models, and collaborate with stakeholders to drive data-driven decisions.

The Data Scientist will support analytics initiatives, collaborating across functions to build models, map data, and solve business problems using data science techniques.

Design and analyze experiments (A/B and observational), apply causal inference and statistical methods, develop and deploy ML models, evaluate LLM/AI solutions, define metrics and measurement frameworks, partner with engineering/product teams, and communicate findings via data storytelling and visualizations.

Lead and deliver complex data science and engineering projects: design data models and scalable pipelines, build and productionize statistical and ML models (Python/R), perform data integration and quality work, generate actionable insights for executive stakeholders, and provide domain leadership in Payment Integrity/FWA.