Top Data Science Jobs in Buffalo, NY

The Data Scientist will analyze user behavior, create segmentation strategies, design experiments, and track business metrics to drive growth in a SaaS company.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Lead end-to-end data science projects, develop machine learning models, collaborate with stakeholders, mentor team members, and communicate insights effectively.

Design and train LLMs and agentic systems for cybersecurity using RLHF/RLAIF and supervised fine-tuning. Build agent architectures, tooling, retrieval/memory, and evaluation pipelines with rigorous statistical benchmarking. Optimize prompts and inference, collaborate with engineers to productionize prototypes, and drive research on agent planning, safety, and reliability.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Work with clients and engineers to audit marketing datasets, build analytics pipelines, and apply predictive models (segmentation, attribution, propensity) using SQL, Python, cloud big-data tools, and ML frameworks. Support implementations, develop analytic applications, and communicate findings to drive marketing outcomes.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

As a Data Scientist, you will develop algorithms, collaborate on product requirements, and communicate data-driven insights, while enhancing product offerings and delivering actionable insights.

Develop and evaluate predictive models and simulation workflows for CHO cell culture and upstream bioprocess digital twin applications. Analyze time-series bioreactor and assay data, implement models in Python, visualize results, collaborate with scientists and engineers, and support experimental planning and model-driven decision making.

Lead development and deployment of bioprocess digital twin models for CHO cell culture and upstream workflows. Build hybrid mechanistic/ML models, enable virtual experiments and forecasting, define data and validation requirements, and support real-time monitoring to detect deviations and recommend corrective actions.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Entefy seeks a Senior Data Scientist with a Master's or PhD in a relevant field, and 3+ years of experience. Responsibilities include delivering advanced analytics, leading teams, and extracting insights from datasets.

As a Data Scientist, you'll build production-grade ML systems for HVAC efficiency, working on scaling models, database optimization, and real-time monitoring systems.

Lead development and deployment of predictive models, analytics, and dashboards to support Sales, Marketing, Customer Success, and GTM operations. Mentor data scientists, partner with business leaders to prioritize analytics, ensure data governance and quality, and present insights to executives. Work with Snowflake/data lakes, build scalable ML solutions, and drive analytics adoption across the enterprise.

Lead data science initiatives to develop and deploy AI solutions that improve business outcomes. Collaborate with cross-functional teams to analyze data, create models, and evaluate performance.

Conduct data science research on experimental methodologies and causal modeling, develop tools to analyze experimental data, prototype improvements to platform features, and communicate results to internal teams and external stakeholders to drive product and research decisions.

Apply advanced analytics, statistical modeling, and machine learning to build and operationalize predictive models for pricing, underwriting, claims, and operations. Partner with business, product, and engineering teams to integrate models, monitor performance, improve data pipelines, and deliver dashboards and insights that inform strategic decisions.

Build lightweight, Slack- and Sheets-integrated AI agents to pull and analyze data from ad and web APIs, surface anomalies and trends, and produce alerts, reports, and LLM-generated content briefs and recommendations. Deliver scoped, fast iterations on multi-step pipelines including classifiers, scorers, and recommendation layers.

Senior individual contributor who drives product analytics, experimentation, and applied modeling. Owns growth and performance analyses, designs experiments with robust statistical evaluation, builds repeatable reporting, and partners with Product, Engineering, Operations, Clinical, and Finance to translate data into actionable product and operational decisions.

Build and deploy NLP solutions: perform EDA on text, design and evaluate models (classification, NER, information extraction), develop pipelines from regex to transformers, apply generative AI and prompt engineering, write production-grade Python, and collaborate with ML engineering to deploy and monitor scalable cloud-based solutions.

Develop and deploy statistical and machine learning models to analyze customer behavior across marketing and digital channels. Design and evaluate experiments/A-B tests, apply causal inference, build scalable Python/SQL pipelines, query Redshift, use SageMaker for production models, maintain version-controlled workflows, and present insights to cross-functional stakeholders.

Partner with a sports-sector client to design, build, and deploy end-to-end machine learning solutions. Own modeling lifecycle, feature engineering, validation, automation, and integration with engineering teams. Use SQL and Python to extract data, build models (including NLP for surveys), and deliver reproducible analytics and measurable business impact.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Design and implement reinforcement learning models and sequential behavioral models to optimize collections and treatment strategies. Build scalable ML pipelines (Databricks), run simulations and offline policy evaluation, and collaborate with stakeholders to deploy adaptive decisioning systems under uncertainty.