Top Software Data Science Jobs

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.

Lead the design and deployment of machine learning, quantitative financial, recommendation, personalization, and generative AI solutions. Architect scalable cloud data pipelines, backend systems, APIs, and ML platforms using Java, Spring Boot, Kafka, SQL, Google Cloud, and LLM technologies. Collaborate with research, engineering, product, and business teams to improve search, discovery, feed recommendations, and financial analytics while shaping AI/ML roadmaps and reference architectures.

Develop and deploy NLP, entity-extraction, and generative AI models for cyber datasets in a classified environment. Responsibilities include parsing logs, feature engineering, model evaluation, data-quality validation, Python workflow development, requirements translation, stakeholder demos, and collaboration with cyber experts and data engineers. The role is full-time and on-site at a secure government facility.

Develop revenue optimization and risk mitigation strategies using experimentation, causal inference, forecasting, Monte Carlo simulations, and machine learning. Build data pipelines, improve analytics infrastructure, generate insights for client-facing teams, and influence Product and Engineering roadmaps. The role requires strong strategic thinking, business impact orientation, and collaboration in a remote-first environment.

Develops and maintains data pipelines, analyzes structured and unstructured data, and applies statistical, machine learning, NLP, forecasting, clustering, and predictive modeling techniques. Uses Python, R, distributed processing frameworks, cloud data platforms, and visualization tools to solve complex national security data challenges. Collaborates with data engineers, automates data processes, develops algorithms, and communicates analytical findings to nontechnical audiences.

Develop advanced data science, analytics, machine learning, and predictive modeling solutions for national security and Intelligence Community missions. Analyze large datasets, identify relationships and trends, build computational models, create visualizations and dashboards, and maintain Python-based tools. Collaborate with analysts, engineers, and mission stakeholders while supporting research, documentation, and evolving mission priorities.

Develop and optimize machine learning models using real-world data. Design graph-based ML algorithms, apply research breakthroughs, fine-tune LLMs within RAG pipelines, and develop supervised learning models. Ensure AI explainability, ethics, scalability, and performance while collaborating with development teams. Understand business requirements, coordinate ML development, communicate results visually, and identify new machine learning applications.