Senior Data Scientist

Responsibilities

• Own the code, not just the model: Design, write, test, and deploy production-grade ML and AI systems using Python, modern ML frameworks, and cloud-native tooling.
• Build generative AI & LLM-powered solutions: Architect and implement RAG pipelines, fine-tuning workflows, agentic systems, and LLM evaluation harnesses.
• Engineer scalable ML pipelines: Develop robust feature engineering, training, inference, and monitoring pipelines built for reliability and scale.
• Ship end-to-end: Take models from prototype through CI/CD into monitored production environments, including automated retraining and drift detection.
• Lead complex analytical investigations: Apply causal inference, Bayesian modeling, survival analysis, and simulation to solve high-stakes business problems.
• Translate ambiguity to impact: Frame undefined problems with entrepreneurial clarity: define success metrics, scope solutions, and move from question to insight at speed.
• Ensure reproducibility and rigor: Establish standards for experiment tracking, version control, and model validation aligned with enterprise governance requirements.
• Rapidly prototype and validate: Move from idea to working proof-of-concept in days, not months using experimentation to de-risk investment before scaling.
• Influence enterprise standards: Shape the organization's model development, validation, and deployment standards as a principal-level technical authority.

Qualifications

• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a closely related quantitative field. Master's or PhD preferred.
• 3-5+ years of hands-on experience in applied machine learning, data science, or AI engineering not just analytics.
• Demonstrated track record of shipping ML models and AI systems to production, including ownership of monitoring and maintenance.
• Experience leading complex, end-to-end data science projects from problem definition through deployment and business impact measurement.
• Proven ability to influence technical direction and strategy without direct management authority.
• Technical Proficiency (Must Be Hands-On):
  • Python (expert-level): NumPy, Pandas, Scikit-learn, PyTorch or TensorFlow, Hugging Face, LangChain/LlamaIndex or equivalent.
  • ML Engineering: Feature stores, model registries (MLflow), experiment tracking, CI/CD for ML, containerization (Docker/Kubernetes).
  • LLMs & Generative AI: Prompt engineering, RAG architecture, fine-tuning, evaluation frameworks, and agentic workflow design.
  • SQL & Data Engineering: Complex query optimization, dbt or similar, working fluently with Spark or Databricks.
  • Cloud Platforms: Azure ML preferred; AWS SageMaker or GCP Vertex AI experience.
  • Statistics & ML Foundations: Regression, classification, clustering, time-series, Bayesian methods, causal inference, and model interpretability (SHAP, LIME).
  • Software Engineering Practices: Git, code review, unit testing, design patterns you write code that others can maintain.

Preferred Qualification

• Experience in financial services, insurance, or other regulated industries with model risk management requirements.
• Contributions to open-source ML projects.
• Experience building and operating real-time inference systems (low-latency APIs, streaming prediction pipelines).
• Familiarity with model governance frameworks and regulatory requirements.
• Experience with agentic AI systems, multi-modal models, or domain-adapted LLMs in an enterprise context.
• Background in agile/product-oriented analytics teams with sprint-based delivery.

Additional Company Details

We do not accept any unsolicited resumes from external recruiting agencies or firms. The company offers a competitive compensation plan and robust benefits package for full-time regular employees which for this role include: Base Salary Range: $150,000 – $200,000 Eligible to participate in annual discretionary bonus. Benefits: Health, Dental, Vision, Life, Disability, Wellness, Paid Time Off, 401(k) and Profit-Sharing plans. The actual salary for this position will be determined by a number of factors, including the scope, complexity and location of the role; the skills, education, training, credentials and experience of the candidate; and other conditions of employment.

Sponsorship Details

Sponsorship not Offered for this Role.