Data Scientist

Role snapshot Title: Data ScientistLocation: Indianapolis, IN (hybrid, 2–3 days on-site)Engagement: Full-time, placed with an Augmented enterprise clientLevel: 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 casesExplore and prepare large, complex data sets; engineer features with data engineering partnersBuild, validate and tune models for prediction, classification, forecasting, segmentation and optimisationDesign and analyse experiments (A/B tests, causal inference) to measure impactDevelop GenAI and LLM-based solutions, including retrieval-augmented generation (RAG), evaluation and guardrailsDeploy models to production with MLOps practices: versioning, CI/CD, monitoring and retrainingEnsure models are explainable, fair, well documented and compliant with regulatory requirementsCommunicate findings clearly through visualisations, presentations and written recommendationsMentor 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 productionMS 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 SQLSolid foundation in statistics, experimental design and ML evaluationHands-on experience with at least one cloud ML platform: Databricks, AWS SageMaker, Azure ML or Vertex AIExperience with MLOps tooling such as MLflow, Git and CI/CDAbility to explain complex results to non-technical audiencesAuthorized to work in the United States Nice to have Production GenAI experience: LLM APIs, vector databases, prompt engineering and LLM evaluation frameworksTime-series forecasting, NLP or computer visionExperience in Life Sciences (clinical, commercial or real-world data) or Financial Services (risk, fraud, credit)Familiarity with model risk management, GxP or other regulatory frameworksSpark or distributed computing for large-scale dataConsulting or client-facing delivery background Tech stack Languages: Python, SQL, R (a plus)ML libraries: scikit-learn, XGBoost / LightGBM, PyTorch, TensorFlowGenAI: LLM APIs, LangChain / LlamaIndex, vector databasesPlatforms: Databricks, Snowflake, AWS SageMaker or Azure MLMLOps: MLflow, Git, CI/CD pipelines, DockerData: Spark, dbt, cloud data lakesVisualization: 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 scaleSmall, senior teams with direct access to client decision-makersExposure to AI-native delivery practices and modern toolingCompensation: [salary range to confirm] plus benefits How to applyApply 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.