What are the responsibilities and job description for the Data Scientist position at Augmented?
- 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
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.
- 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
- 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
- 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
- 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
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
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.