Top Data Science Jobs

Seeking a Senior Data Scientist to lead predictive analytics and machine learning initiatives. Responsibilities include building ML models, data management, and mentoring junior data scientists. Must have expertise in AWS and strong statistical knowledge.

Lead development, monitoring, and enhancement of credit risk models and loss forecasting. Extract and prepare large datasets, perform EDA, build pipelines and dashboards, automate reporting, and provide actionable insights to stakeholders. Maintain audit-ready documentation and participate in governance, validation, and scenario/stress analyses to manage portfolio risk.

The Principal Data Scientist develops predictive models, designs large-scale data pipelines, and collaborates to enhance data-driven decision-making using advanced analytical algorithms.

As a Data Scientist at Sardine, you'll design and deploy data-driven solutions to prevent fraud, collaborating with clients and engineering teams to build and optimize machine learning models.

The Senior Data Scientist will design and deploy scalable ML solutions, develop NLP systems, and oversee the ML lifecycle while collaborating with cross-functional teams and mentoring others.

As a Senior Data Scientist, you will develop and manage production-grade forecasting systems for financial metrics, collaborating across teams to improve accuracy and stability while mentoring peers.

Lead high‑impact data science initiatives across Global Operations, building production‑grade analytics, models, and dashboards (primarily in R), using Snowflake and Posit platforms, applying advanced statistics, ML, and LLM techniques, mentoring junior staff, and presenting insights to senior leadership to reduce supply chain risk and optimize manufacturing.

The Data Scientist Level 3 will analyze large datasets, develop strategies for data extraction, and communicate findings. Required experience includes machine learning and programming.

Lead product-focused adoption analytics by sourcing and analyzing enterprise telemetry, designing scalable data pipelines, building BI dashboards and executive narratives, conducting forensic investigations, and partnering cross-functionally to measure and optimize product and AI agent impact.

Lead development and deployment of analytical models, metrics, and tools for Hockey Operations. Produce visualizations and reports, communicate insights to coaches and decision-makers, mentor junior analysts, collaborate to deliver production-ready data products, and advance the organizations hockey analytics capabilities.

Build Scout Motors' first enterprise probabilistic demand forecast from reservations and limited signals, solve cold-start forecasting, own demand data and pipelines end-to-end, productionize models with engineering into the IBP product, set up monitoring and measurement, and translate results for cross-functional stakeholders.

Lead growth-focused data science initiatives, owning LTV prediction models across countries and channels. Translate business questions into models, analyze large customer and marketing datasets, evaluate acquisition quality, monitor and productionise models with engineering partners, and drive data-informed growth and retention decisions.

Lead ad-quality analytics: build data pipelines, dashboards, and automated reports; design and analyze A/B tests and causal inference studies; develop forecasting and modeling solutions; partner with product, engineering, and business stakeholders to translate findings into executive recommendations and drive ad product improvements.

Lead ad-quality and ad-product analytics: build pipelines, dashboards, and automated reports; design and analyze A/B tests and causal inference studies; develop models and alerts; translate findings into strategic recommendations for stakeholders and executives.

Senior Data Scientist with 10–15 years (MS/PhD) to design and develop ML/NLP models, manage model lifecycle, implement recommendation and pattern-recognition algorithms, use R/Python/SPSS, communicate results to leadership. Logistics/shipment processing experience and cognitive computing are desirable.

Senior data scientist role requiring 10–15 years and an MS/PhD to build and deploy ML models (NNs, SVM, Random Forest, collaborative filtering), lead NLP/text-mining projects, use R/Python/IBM SPSS, manage model lifecycle, communicate results to management. Logistics/shipping and cognitive computing experience are desirable. Contract role requiring US work authorization.

The Senior Data Scientist will design, develop, and deploy AI/ML capabilities for US Government customers, including machine learning algorithms and generative AI applications. Responsibilities include managing data pipelines, conducting R&D, and supporting pre-sales efforts.

Seeking a Senior Data Scientist to design, code, and improve models and algorithms, evaluate decision systems, and partner with teams to translate data into impactful behaviors.

Lead marketing measurement and optimization by building statistical and ML models (propensity, forecasting, LTV), attribution methods, and causal inference analyses. Design experiments, automate ETL and reporting pipelines, create dashboards, and translate insights into strategic recommendations to drive advertiser acquisition, retention, and revenue growth.

Lead data strategy and analytics for cognitive assessment studies: design algorithms and models, implement CDISC-compliant data transfers (SDTM/ADaM), produce statistical analysis plans and scientific reports, build and maintain analytics pipelines, dashboards, and visualizations, ensure data quality and QC, and present results to sponsors while managing multiple projects independently.

Lead development and operationalization of data-driven financial forecasting (time-series, AI/ML, causal, Monte Carlo). Integrate statistical outputs into Anaplan planning, establish model governance/validation, build templates/dashboards, collaborate with FP&A and Data/IT, and create a Center of Excellence for advanced forecasting and repeatable playbooks.

Embed in a core business area to define the right problems, design metrics, run analyses and experiments, build reports, surface insights, and drive data-informed decisions end-to-end to improve product, growth, AI/automation, or partnerships.

Embed in a business unit to define analytical problems, build metrics/reports/experiments, pull and interpret data, surface insights, and drive recommendations to improve product, growth, AI/automation, or partnerships.

Embed within a core business area to define problems, build metrics and analyses, run experiments, generate insights from data, and drive decisions end-to-end to improve product, growth, AI/automation, or partnerships.

Embed with a core business area to define problems, build metrics, run analyses and experiments, deliver data-driven recommendations, and drive outcomes end-to-end across growth, product, AI, or partnerships.