Top Software Data Science Jobs

Develops data-driven analytics, dashboards, automated workflows, applications, and integrated tracking systems using Microsoft Power Platform and SharePoint. Translates ambiguous stakeholder needs into scalable data models, reporting tools, and process automation solutions. Maintains and improves dashboards, workflows, and applications while monitoring program performance, data quality, and operational accountability.

Leads end-to-end research, development, validation, and maintenance of statistical and machine learning models for media measurement. Analyzes large datasets, translates findings into product insights, collaborates with engineering and product teams on scalable model deployment, and communicates results through visualizations and presentations. The role requires advanced statistical expertise, machine learning proficiency, Python, and independent project ownership.

Leads data science, analytics, data engineering, and AI/ML teams supporting USCENTCOM operations. Oversees data pipelines, governance, statistical modeling, machine learning, generative AI, data lakes, and operationalization of analytical capabilities. Translates mission requirements into technical solutions, advises senior stakeholders, briefs operational leaders, and manages multiple strategic initiatives. Requires extensive military, federal, or intelligence-community experience, active Top Secret clearance with SCI eligibility, and occasional domestic and international travel.

Own Berry Street’s growth measurement and investment decisions through experimentation, causal inference, attribution, media mix modeling, forecasting, and predictive modeling. Build production data models and automated ad optimization systems using messy marketing, product, and warehouse data. Guide channel budgets, creative, patient acquisition, LTV, and CAC while partnering with Growth, Product, Engineering, and leadership to drive durable cross-functional improvements.

Leads pharmaceutical commercial analytics and data science initiatives supporting brand, marketing, and field teams. Develops machine learning, statistical, predictive, segmentation, targeting, forecasting, and causal models using pharmaceutical commercial data. Translates analyses into recommendations, identifies practical AI opportunities, supports pilots, and drives adoption across business stakeholders. Partners cross-functionally on launch readiness, opportunity sizing, performance tracking, governance, privacy, and compliance while providing subject-matter expertise in pharmaceutical data and commercial strategy.

Develop recommendation, ranking, search relevance, and personalization models for digital shopping experiences. Apply modern machine learning and generative AI techniques, design offline and online evaluations, analyze experiments and customer cohorts, and partner with Product and Engineering on measurable outcomes. Build reusable personalization capabilities, move models toward production evaluation, mentor scientists, and improve technical and experimental practices across the team.

Develop and deploy enterprise machine learning solutions across the full data science lifecycle. Build predictive models, pipelines, and analytical applications; monitor model performance; and support data-driven decisions. Collaborate with stakeholders, engineers, developers, and product teams using cloud platforms, large-scale data, and modern MLOps practices. Responsibilities include statistical analysis, feature engineering, model evaluation, data exploration, deployment, optimization, and translating business needs into scalable recommendations.

Conduct healthcare data experiments, feasibility analyses, and evaluations of new data sources to improve audience-building capabilities. Translate client and business priorities into rigorous analyses and recommendations, collaborating independently with client success, product, engineering, and partnerships teams. Deliver client-facing results using healthcare claims, EHR, or similar data while supporting product requirements and commercialization outcomes.

Develops advanced analytics, dashboards, data-entry solutions, and automated tools to support operational strategy and execution. Facilitates performance reviews, improves data quality, coaches stakeholders on data literacy, translates analytics into actionable insights, and leads cross-functional continuous-improvement initiatives across manufacturing and operational teams.

Analyze user behavior and buying intent using statistical modeling and machine learning. Build predictive models for personalization, churn, segmentation, and recommendations; design A/B tests; collaborate with product, engineering, marketing, and data engineering teams; communicate insights through presentations and visualizations; and monitor model performance in production.