Top Senior Data Science Jobs

Develop and deploy predictive, statistical, and machine learning models for enterprise business decisions, including forecasting, segmentation, propensity modeling, optimization, and fraud detection. Partner with cross-functional stakeholders, improve cloud analytics infrastructure, evaluate data sources, communicate insights, and mentor junior data scientists and analysts.

Lead end-to-end dynamic pricing models using demand modeling, causal inference, market simulation, and optimization. Develop pricing approaches for elasticity, seasonality, pacing, and lead-time dynamics; design experiments measuring marketplace effects; and translate complex business problems into deployed solutions. Own production Python code, model deployment, monitoring, retraining, reproducibility, and ML operations while influencing product strategy and collaborating with technical and executive stakeholders.

Own mobile marketing analytics frameworks, dashboards, cohort and campaign analysis, profitability modeling, audience strategy, and incrementality experiments. Analyze attribution, retention, LTV, CAC, ROAS, payback, creative performance, and marketplace dynamics. Partner with marketing, finance, and leadership to guide spend allocation and growth decisions while navigating mobile privacy and attribution challenges.

Develops advanced data science solutions for Intelligence Community missions, including scalable data pipelines, automation, data modeling, statistical analysis, visualizations, dashboards, and production-quality Python applications. Transforms complex intelligence datasets into actionable insights, modernizes analytic workflows, supports operational decision-making, and collaborates with analysts, engineers, and mission stakeholders. The role also provides technical leadership, manages concurrent initiatives, and may apply geospatial analytics, machine learning, cloud platforms, and distributed computing.

Develops machine learning and deep learning models, analyzes large structured and unstructured datasets, identifies trends, and creates predictive solutions for business decisions. The role assesses AI/ML feasibility, builds data processes and prediction systems, provides analytical recommendations, collaborates with product teams and stakeholders, and supports innovative data strategies while following the bank’s risk management framework.

Develop and deploy advanced data science, machine learning, generative AI, and intelligent agent solutions across Salesforce and enterprise platforms. Build predictive models, conversational AI, data pipelines, and autonomous workflows for customer engagement, sales, and service operations. Partner with business and technical teams to define use cases, operationalize responsible AI, govern models, and deliver measurable outcomes. The role also involves evaluating emerging technologies, communicating insights to executives, mentoring developers, and establishing reusable technical standards.

Designs and deploys advanced machine learning, predictive analytics, generative AI, conversational AI, and intelligent agent solutions integrated with Salesforce. Builds data pipelines, features, models, and autonomous workflows using Agentforce, Einstein AI, and Data Cloud. Partners with business, product, engineering, and Salesforce teams to define use cases, govern models, monitor performance, and deliver measurable outcomes. The role also requires executive communication, technology evaluation, responsible AI practices, and mentoring developers.

Own end-to-end data science projects, including customer discovery, statistical analysis, predictive modeling, Python and SQL development, Databricks pipelines, AI-enabled analytics, quality checks, and customer presentations. Build reusable data assets at claims-data scale and contribute to Bonfire’s AI-powered products. The role requires rigorous analysis, strong communication, customer empathy, and comfort working with ambiguous problems.

Provides data science consulting support to DHS by developing Power BI dashboards, cleaning and transforming data with Python and R, maintaining analytics solutions, and delivering predictive analytics and business intelligence. The role involves client communication, ad hoc analysis, requirements gathering, business development support, and translating technical topics for nontechnical audiences. Familiarity with cloud analytics platforms, PowerApps, ETL/ELT, financial data, and workload modeling is beneficial.

Leads the design, development, deployment, and monitoring of machine learning and AI solutions. Frames complex business problems, engineers features, trains and validates models, conducts fairness and bias assessments, deploys production systems, and monitors drift and performance. Communicates results to leadership, mentors junior team members, and provides technical thought leadership on scalable ML solutions.

Leads end-to-end applied data science and AI workstreams for predictive underwriting and pricing, including problem framing, feature engineering, model development, validation, deployment, monitoring, and business-impact measurement. Builds explainable, governed, and reusable production models using healthcare and insurance data. Partners with underwriting, business, product, engineering, and legal teams; mentors others; and advances responsible AI, MLOps, model governance, and shared data science practices.

Build and deploy machine learning models for supply chain, inventory, demand forecasting, pricing, promotion, clearance, and network optimization. Develop time series, predictive, simulation, and discrete optimization models; create scalable pipelines using Python, Spark, and cloud infrastructure; and own the full model lifecycle. Partner with engineering, product, and operations teams, communicate insights, develop decision-support tools, mentor junior data scientists, and measure business impact.

Own end-to-end data science initiatives across growth, personalization, pricing, market execution, risk, financial integrity, and financial crime. Build causal inference, predictive, ranking, and fraud detection models; deploy and monitor them in production; communicate uncertainty and insights to senior stakeholders; and mentor teammates on statistical and modeling best practices.

Develop model-ready datasets and applied machine learning models using biologics assay, biophysical, antibody, protein sequence, and construct data. Curate and version datasets, create featurization and evaluation frameworks, and collaborate with data engineers, discovery scientists, MLOps teams, and molecular modelers to support trustworthy, reproducible biologics discovery and prioritization.

Own applied statistical analysis for mineral processing operations, including experimental design, metric definitions, uncertainty quantification, process control, forecasting, reporting, and dashboards. Analyze messy sensor, assay, laboratory, and plant data to support production, cost, supplier, and capacity decisions. Partner with data platform teams, improve analytical rigor across the company, and communicate findings clearly to operators, engineers, and executives.

Develop analytical and AI-enabled capabilities for a federal data modernization effort. Translate stakeholder questions into statistical analyses, predictive models, forecasts, and scenario analyses; build governed datasets using Databricks, BigQuery, Python, SQL, and PySpark; validate and document models; maintain analytical products; and communicate visualizations, dashboards, findings, assumptions, and limitations to technical and non-technical audiences.

Lead data science research and production development for attribution, identity resolution, tracking quality, and incrementality measurement. Design experiments, build and monitor production pipelines, evaluate third-party identity vendors, and develop ML and graph-based solutions. Translate technical findings into recommendations for Product, Engineering, and leadership while shaping measurement strategy and build-versus-buy decisions.

Designs, builds, evaluates, and scales generative AI tools, agents, workflows, and analytical models for Customer Experience. Responsibilities include prototyping AI capabilities, developing production-ready solutions, supporting enablement and training, communicating insights, managing projects, mentoring analysts, and partnering with business stakeholders. The role requires advanced statistical modeling, GenAI expertise, data storytelling, and strong communication skills.

Analyze complex structured and unstructured data, build and maintain data pipelines, develop predictive and prescriptive models, and manage machine learning projects. The role uses statistical methods, NLP, RPA, text mining, forecasting, and cloud-based data platforms to support Special Operations Command North. Responsibilities include data engineering, algorithm development, visualization, experimentation, digital transformation, and communicating analytical findings to nontechnical stakeholders.

Develop and deploy data science solutions for financial crime and fraud prevention. Responsibilities include understanding, cleaning, validating, and preprocessing client data; engineering features; tuning machine-learning models; optimizing runtime performance; communicating findings; collaborating with client stakeholders and internal teams; and improving data science processes, tooling, and best practices.

Develop and maintain product-focused data science and machine learning models for land-use suitability, land valuation, comparable transactions, and community sentiment. Source and curate geospatial and market datasets, design evaluation frameworks, improve existing models, and collaborate with design, product, and engineering to deliver customer-facing insights. The role requires production-quality Python, SQL, geospatial tooling, reproducible workflows, and the ability to explain analytical outputs to non-experts.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

Lead Data Scientist partners with business leaders to identify opportunities, design, develop, and deploy advanced analytics and ML solutions. Responsibilities include data preparation, modeling, feature engineering, delivering insights and visualizations, establishing standards, and directing associate data scientists, contractors, and consultants to produce measurable business value.

Lead data science and analytics initiatives supporting product, engineering, leadership, and insurance clients. Analyze claims and product data, design experiments, generate hypotheses, measure ROI, build dashboards and reproducible analytics, and communicate actionable recommendations. Mentor junior analysts, conduct client-facing analyses, collaborate with ML teams, investigate data issues, and shape product strategy through sophisticated statistical modeling and exploratory analysis.