Senior Data Scientist
Resumo da vaga pela JobGrid
Senior Data Scientist at Tiger Analytics Inc.: St. Louis, Estados Unidos; Presencial; Tempo inteiro; Sénior; TI. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: St. Louis, Estados Unidos, Presencial
- Role classification: TI, Data Science e ML, Tempo inteiro, Sénior
- Source freshness: checked by JobGrid on 2026-06-07.
- Application path: candidates continue to the employer application page with non-personal referral tags.
Tiger Analytics is looking for experienced Data Scientists to join our fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.
As a Data Scientist you will be at the forefront of solving high-impact business problems using advanced machine learning, data engineering, and analytics solutions. The role demands a balanced mix of technical expertise, stakeholder management. You will design and analyze A/B tests and apply advanced techniques such as causal inference, matching models, and AutoML to generate reliable, actionable insights. Partnering closely with business and cross-functional teams, you will translate hypotheses into robust analytical models, validate outcomes with statistical rigor, and clearly communicate results to drive data-backed decision-making and measurable business value.
Key Responsibilities
- Apply statistical techniques and machine learning methods to solve complex business problems.
- Build, validate, and deploy predictive and analytical models using Python.
- Perform data extraction, transformation, and analysis using SQL across large datasets.
- Work on causal inference techniques such as causal ML, matching models, or uplift modeling to evaluate business interventions.
- Collaborate with product, business, and engineering teams to translate requirements into scalable data solutions.
- Present insights, findings, and recommendations clearly to technical and non-technical stakeholders.
- Ensure data quality, model performance, and continuous improvement of analytical workflows.