Top Software Data Science Jobs

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

The Data Scientist will analyze large datasets, design statistical systems, and build models to aid government clients in decision-making. Responsibilities include applying predictive modeling, optimizing processes, and collaborating on agile teams.

As a Data Scientist, you will analyze diverse data to build digital twins of electric grids, advise on strategy, and enhance models using AI and ML techniques.

Lead the evolution of Unity's Ads Experimentation platform by bridging advanced statistical methodologies with engineering to optimize ad delivery and model validation.

As an Applied Data Science Intern, you will work on data innovation projects, focusing on AI-driven solutions, machine learning pipelines, and enterprise integrations while receiving mentorship from experienced professionals.

The Data Scientist will create and deliver analytic processes and data solutions while coordinating with clients to address data-related issues. Responsibilities include designing data architecture, building ETL pipelines, integrating APIs, conducting impact analysis, and preprocessing geospatial data.

As a Staff Data Scientist, you will lead experimentation strategies, apply advanced causal inference techniques, and guide analytical insights to drive product and business decisions.

Onsite Data Scientist supporting intelligence-focused analytics and product development. Build Power BI dashboards, manipulate data with Python/SQL, create visualizations and lightweight web apps, communicate findings to nontechnical audiences, and translate customer requirements into specifications.

Own revenue and product analytics: build KPIs, dashboards, dbt-style data models, run product/usage experiments, partner with GTM, and deliver executive insights to scale revenue operations.

Turn raw customer supply-chain data into structured representations and models. Interpret and contextualize noisy data, form and test hypotheses, build inference, regression, simulation and optimization logic, validate model quality in production, and translate scientific requirements into engineering specifications while collaborating with data engineers and supply-chain experts.