Top Software Data Science Jobs

Develop reproducible emissions calculations and analytical methodologies using Python, SQL, and PySpark. Investigate complex energy and emissions datasets, identify data-quality issues, apply statistics and machine learning, and translate research into scalable production capabilities. Collaborate with engineering, product, research, and customer teams while contributing maintainable code through Git, testing, documentation, and code review.

Conduct exploratory data analysis, define business objectives and success metrics, develop advanced SQL and analytical workflows, build predictive and forecasting models, create dashboards, and translate findings into actionable recommendations. The role requires presenting data stories to technical and non-technical stakeholders, designing experiments, and independently driving analytical initiatives using large-scale datasets.

Owns end-to-end data science for growth, product, AI and automation, or strategic partnerships. Responsibilities include defining business problems, developing metrics and reports, analyzing data, designing experiments, communicating insights, influencing leadership decisions, and driving measurable business outcomes. Requires strong SQL or Python skills, independent analytical ability, comfort with ambiguity, persuasive communication, and ownership of strategic initiatives.

Analyze recruiting and interview data to improve AI-powered candidate evaluation systems. Build metrics, statistical and machine learning models, LLM evaluation datasets, experiments, dashboards, and reproducible data workflows. Collaborate with product and engineering teams on candidate matching, interview quality, recruiting analytics, bias detection, and AI system improvements.

Analyze product and operational data to guide roadmap decisions, define metrics, evaluate experiments, uncover user friction, and build reliable dbt models, dashboards, and analytical tools. The role partners closely with Product and Operations, improves data quality and instrumentation, and translates complex findings into actionable recommendations. It requires strong SQL, Python, experimentation, data modeling, visualization, and product judgment skills.

Supports advanced data analysis and industry insights for ecommerce growth projects. Responsibilities include partnering with stakeholders to scope and build models and data pipelines, performing data wrangling, evaluating model performance, and promoting data science tools and best practices. The intern will work under mentorship from the Data Science team during a 12-week hybrid summer internship.

Build and maintain large-scale systems to extract, connect, retrieve, and reason over web knowledge. Implement petabyte-scale data pipelines, optimize for correctness/reliability/scale, and improve data quality to power factual QA and search.

Data Science Interns will use Databricks business and operations data to generate insights for product design, customer acquisition and retention, and engineering efficiency. Responsibilities include collaborating with cross-functional stakeholders, applying statistical and machine learning methods such as causal inference and recommender systems, managing end-to-end projects, presenting findings, and deploying algorithms. Candidates will work with SQL and Python and pursue a quantitative Master’s or PhD degree.

Conduct defensive cyber threat hunts and statistical analyses across mission data platforms. Build and test models, assess cyber data quality, develop dashboards and visualizations, and present actionable findings. Support threat-hunting workflows, detection content, customer training, and operational requirements. Help automate repeatable analytic processes while collaborating with defense cyber customers. This is a fully onsite Fort Meade role with travel up to 40% domestically and internationally.

Develop and implement agentic and generative AI solutions, including LLMs, RAG, AI security, and related platforms. Support research and development, technical proposals, prototyping, and client consulting. Apply data science and engineering best practices, integrate and analyze data, gather requirements, and translate technical concepts for clients and stakeholders.