Senior Data Scientist

Tenth Revolution Group

Warsaw, IN, United States

13 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

5 years minimum

Working hours

Regular working hours

Languages

English

Job source

arc.dev

Tech stack

Amazon Web Services Microsoft Azure Python (Programming Language) Machine Learning NumPy Standard Sql Software Engineering Large Language Models Multi-Agent Systems Prompt Engineering Apache Spark Model Validation

+6 more

Generative AI Pandas Git Flow Scikit Learn Software Version Control Databricks

Job description

Translate business needs into data science problems, hypotheses, metrics, and evaluation plans.
Design and evaluate GenAI and ML solutions, including LLMs, RAG, agents, embeddings, forecasting, classification, and regression.
Build robust evaluation frameworks, including offline/online testing, human review, hallucination and groundedness checks, and error analysis.
Develop production-quality Python code with testing, documentation, version control, and reproducibility.
Monitor models and AI systems for drift, performance, cost, latency, and traceability.
Collaborate with product, engineering, data, and business teams to deliver production-ready solutions.
Communicate insights, risks, trade-offs, and recommendations clearly to stakeholders., Benefits may include private medical care, flexible benefits, insurance, pension contributions, home-office support, and additional paid holidays.

Requirements

Master’s or PhD and 5+ years of experience in Data Science or Applied ML.
Strong Python and SQL skills; experience with pandas, NumPy, and scikit-learn.
Hands-on experience with Generative AI, including embeddings, prompt engineering, tool calling, RAG, and agent frameworks such as LangChain, LangGraph, or PydanticAI.
Strong foundation in classical ML, model validation, metrics, and experimentation.
Experience with cloud platforms such as AWS and/or Azure, plus Databricks or Spark.
Good software engineering practices, including testing, documentation, and Git-based workflows.
Strong analytical, problem-solving, and stakeholder communication skills.
Fluent English.