Data Scientist II

Primary Duties and Responsibilities

• Analyze data to support (or disprove) a thesis – dig into data, form hypotheses, and let evidence guide conclusions.
• Select and implement the right tools for the job – choose between transformers, gradient‑boosting models, or other techniques as appropriate.
• Build, train, test, and validate models – handle algorithm selection, hyper‑parameter tuning, and rigorous evaluation.
• Engineer models into production – ensure models run reliably on real infrastructure and serve real customers.
• Document work – maintain clear documentation for models, testing protocols, and decision rationale.
• Monitor and improve models in production – detect drift, data changes, and determine when to retrain or rebuild.
• Explore agentic and reasoning systems – help evaluate semi‑autonomous systems that can plan and act.
• Perform other duties as assigned or apparent.

• Bachelor’s Degree or equivalent experience.
• 2+ years of prior work‑related experience.

• 2‑5+ years of hands‑on AI experience, including working with LLMs via API/SDK and deploying ML/DL models in production.
• Strong foundation in linear algebra, calculus, probability, and statistical inference.
• Understanding of prompt engineering, RAG architectures, fine‑tuning approaches, and embedding models.
• Command of supervised and unsupervised learning techniques: regression, classification, clustering, dimensionality reduction, ensemble methods.
• Ability to evaluate LLM outputs critically and design guardrail systems.
• Familiarity with tokenization, context windows, and inference optimization.
• Deep learning expertise in CNNs, RNNs/LSTMs, transformers, and attention mechanisms.
• Experience implementing reinforcement learning algorithms (Q‑learning, policy gradients, actor‑critic, or multi‑armed bandits).
• Knowledge of reward shaping, exploration vs. exploitation trade‑offs, and temporal difference learning.
• Ability to select the appropriate model based on business requirements.
• Experience with model testing frameworks, evaluation, validation strategies, and documentation.
• Strong Snowflake/SQL skills and experience with large datasets.
• Proficiency with pandas, Num Py, and data manipulation at scale.
• Data quality assessment, cleaning, and validation expertise.
• Clean, production‑grade Python coding skills.
• Familiarity with ML pipelines, feature engineering, and data preprocessing at scale.
• Understanding of model serving patterns: batch inference, real‑time APIs, streaming.
• Experience deploying to production and maintaining models over time.
• Working knowledge of AWS services (Bedrock, Sage Maker, Lambda, S3, EC2, Step Functions, Cloud Watch, EKS).
• Containerization with Docker and basic orchestration.
• Infrastructure‑as‑code using CDK or Terraform.
• Git version control and collaborative development practices.
• Experience with Atlassian JIRA, Confluence, Slack, Jupyter notebooks.
• Proficiency in Python libraries:
  PyTorch, Tensor Flow, scikit‑learn, XGBoost, LightGBM, Auto Gluon, Cat Boost.
• Experiment tracking tools: MLflow, Weights & Biases, or similar.

• Building autonomous or semi‑autonomous AI systems.
• Familiarity with agent frameworks (Strands, Agent Core, Lang Chain) and planning algorithms.
• Experience with image classification, object detection, or segmentation.
• Knowledge of transfer learning and pretrained vision models.
• Experience in real estate, mortgage, financial services, or logistics.
• Experience with valuation models, risk scoring, or pricing algorithms.
• Familiarity with time‑series forecasting or geospatial analysis.
• CI/CD pipelines for ML workflows.
• Model versioning, A/B testing frameworks, and canary deployments.
• Monitoring, alerting, and drift detection in production.
• Experience with model documentation and governance requirements.

• Competitive Compensation: anticipated base salary from $98,000 to $148,000 based on skills and experience, with eligibility for an annual incentive program.
• Paid Time Off: 25 days of paid time off annually (prorated) plus 9 paid holidays and 2 floating holidays.
• Parental Leave: offered to all new parents.
• Health Benefits: multiple medical plan choices including HSA and FSA options,…