Data Scientist

Role snapshot Title: Data Scientist Location: Indianapolis, IN (hybrid, 2–3 days on-site) Engagement: Full-time, placed with an Augmented enterprise client Level: Intermediate to Senior (4+ years in data science or applied ML) Reports to: Data Science / Analytics Lead About the role Augmented is hiring a Data Scientist to build and deploy machine learning and GenAI solutions for one of our large enterprise clients in a regulated industry.

You will turn business questions into models that ship, from exploration through production and monitoring. You will join a small, senior delivery team that works directly with client business and technology stakeholders. The work spans predictive modelling, experimentation and LLM-based applications, with a strong focus on measurable business impact.

Key responsibilities

Partner with business stakeholders to frame problems, define success metrics and prioritise use cases Explore and prepare large, complex data sets; engineer features with data engineering partners Build, validate and tune models for prediction, classification, forecasting, segmentation and optimisation Design and analyse experiments (A/B tests, causal inference) to measure impact Develop GenAI and LLM-based solutions, including retrieval-augmented generation (RAG), evaluation and guardrails Deploy models to production with MLOps practices: versioning, CI/CD, monitoring and retraining Ensure models are explainable, fair, well documented and compliant with regulatory requirements Communicate findings clearly through visualisations, presentations and written recommendations Mentor junior team members and contribute to reusable tools and best practices Required qualifications 4+ years in data science or applied machine learning, with models delivered to production MS or PhD in Statistics, Computer Science, Mathematics, Engineering or a related quantitative field (or equivalent experience) Strong Python (pandas, scikit-learn, and PyTorch or TensorFlow) and advanced SQL Solid foundation in statistics, experimental design and ML evaluation Hands-on experience with at least one cloud ML platform: Databricks, AWS SageMaker, Azure ML or Vertex AI Experience with MLOps tooling such as MLflow, Git and CI/CD Ability to explain complex results to non-technical audiences Authorized to work in the United States Nice to have Production GenAI experience: LLM APIs, vector databases, prompt engineering and LLM evaluation frameworks Time-series forecasting, NLP or computer vision Experience in Life Sciences (clinical, commercial or real-world data) or Financial Services (risk, fraud, credit) Familiarity with model risk management, GxP or other regulatory frameworks Spark or distributed computing for large-scale data Consulting or client-facing delivery background Tech stack Languages: Python, SQL, R (a plus) ML libraries: scikit-learn, XGBoost / LightGBM, PyTorch, TensorFlow GenAI: LLM APIs, LangChain / LlamaIndex, vector databases Platforms: Databricks, Snowflake, AWS SageMaker or Azure ML MLOps: MLflow, Git, CI/CD pipelines, Docker Data: Spark, dbt, cloud data lakes Visualization: Power BI, Tableau, Plotly / Streamlit Why Augmented Augmented is an AI-native engineering delivery hub serving large enterprises in Life Sciences and Fintech, with teams in Toronto and the US.

Build ML and GenAI solutions that reach production at enterprise scale Small, senior teams with direct access to client decision-makers Exposure to AI-native delivery practices and modern tooling Compensation: [salary range to confirm] plus benefits How to apply Apply through this posting with your resume and a short note on a model you took to production (problem, approach, measured impact).

Augmented is an equal opportunity employer. Accommodations are available on request at any stage of the hiring process.