Top Software Data Science Jobs

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.

Develop statistical and machine learning models using soccer tracking and event data to support player recruitment decisions. Build spatio-temporal models, metrics, analyses, and interpretation tools; validate models; communicate findings to professional club clients; and partner with engineering on cloud deployment and production reliability. The role also includes code review, model maintenance, and advancing analytical methods.

Drive product analytics across feature adoption, usage, pricing, customer health, and experimentation. Partner with product, engineering, and business leaders to identify opportunities, deliver actionable insights, improve self-serve analytics and governance, and strengthen data pipelines and analysis quality. The role requires communicating complex findings clearly and working from Aircall’s San Francisco office two days per week.

Conduct quantitative UX research for a complex enterprise platform, including surveys, benchmarking, segmentation, statistical analysis, behavioral telemetry, and longitudinal measurement. Define and audit experience metrics and instrumentation, integrate survey, product, and business data, assess validity and limitations, and communicate evidence-backed findings to product, UX, and leadership teams. Coach teams and review quantitative research to support roadmap and planning decisions.

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.

Own AI quality for an agentic enterprise platform, including evaluation datasets, grading rubrics, regression testing, model benchmarking, cost optimization, observability, and feedback systems. Partner with engineering, product, design, sales, and client-facing teams to diagnose failures, improve prompts and models, define quality standards, and ship product changes. The role combines hands-on Python and SQL analysis with product management, LLM evaluation, RAG, document AI, and agent workflow expertise.

Develop statistical models, forecasting methods, and machine learning algorithms for airline optimization and travel retailing. Source and manipulate data using multiple programming languages, deploy models in cloud environments, and collaborate with software engineers on production analytics tools. Define KPIs, monitor data and model performance, conduct proof-of-concept studies, and communicate insights to clients and internal stakeholders.

Develop graph data models, optimize Neo4j and Cypher queries, build Python data parsers, validate mission data, and support near-real-time graph analysis. Translate analyst and customer requirements into graph-based solutions, collaborate with software developers, document schemas and processes, and address operational challenges through data modeling and graph technologies.