Data Scientist

Role Summary

We are seeking an experienced professional with strong expertise in Data Science and machine learning engineering, with hands‑on experience in designing and deploying ML solutions in production. This role focuses on building scalable ML solutions, product ionizing models, and enabling robust ML platforms for enterprise‑grade deployments.

This role is a hybrid work model (4 days in office, 1 day work from home) based out of our corporate headquarters located in Raleigh, NC.

• Build ML Models: Design and implement predictive and prescriptive models for regression, classification, and optimization problems. Apply advanced techniques such as structural time series modeling and boosting algorithms (e.g., XGBoost, LightGBM).
• Train and Tune Models: Develop and tune machine learning models using Python, PySpark, Tensor Flow, and PyTorch.
• Collaboration & Communication: Work closely with stakeholders to understand business challenges and translate them into data science solutions. Collaborate with cross‑functional teams to ensure successful integration of models into business processes.
• Monitoring & Visualization: Rapidly prototype and test hypotheses to validate model approaches. Build automated workflows for model monitoring and performance evaluation. Create dashboards using tools like Databricks and Palantir to visualize key model metrics like model drift and Shapley values.
• Productionize ML: Build repeatable paths from experimentation to deployment (batch, streaming, and low‑latency endpoints), including feature engineering, training, and evaluation.
• Own ML Platform: Stand up and operate core platform components—model registry, feature store, experiment tracking, artifact stores, and standardized CI/CD for ML.
• Pipeline Engineering: Author robust data/ML pipelines (orchestrated with Step Functions / Airflow / Argo) that train, validate, and release models on schedules or events.
• Observability & Quality: Implement end‑to‑end monitoring, data validation, model/drift checks, and alerting SLA/SLOs.
• Governance & Risk: Enforce model/version lineage, reproducibility, approvals, rollback plans, auditability, and cost controls aligned to enterprise policies.
• Partner & Mentor: Collaborate with on‑shore/off‑shore teams; coach data scientists on packaging, testing, and performance; contribute to standards and reviews.
• Hands‑on Delivery: Prototype new patterns; troubleshoot production issues across data, model, and infrastructure layers.

• Education: Bachelor's degree in Computer Science, Information Technology, Data Science, or Mathematics, Statistics or related field. MS Preferred.
• Programming: 5 years experience with Python (pandas, PySpark, scikit‑learn; familiarity with PyTorch/Tensor Flow helpful), bash, experience with Docker.
• ML Experimentation: Design and implement predictive and prescriptive models for regression, classification, and optimization problems.
• ML Tooling: 5 years experience with Sage Maker (training, processing, pipelines, model registry, endpoints) or equivalents (Kubeflow, MLflow/Feast, Vertex, Databricks ML).
• Pipelines & Orchestration: 5 years' experience with Databricks DABS or Airflow or Step Functions, e‑driven designs with Event Bridge/SQS/Kinesis.
• Cloud Foundations: 3 years experience with AWS/Azure/GCP on various services like ECR/ECS, Lambda, API Gateway, S3, Glue/Athena/EMR, RDS/Aurora (PostgreSQL/MySQL), DynamoDB, Cloud Watch, IAM, VPC, WAF. GCP experience is preferred.
• Snowflake Foundations: Warehouses, databases, schemas, stages, Snowflake SQL, RBAC, UDF, Snowpark.
• CI/CD: 3 years hands‑on experience with Code Build/Code Pipeline or Git Hub Actions/Git Lab; blue/green, canary, and shadow deployments for models and services.
• Feature Pipelines: Proven experience with batch/stream pipelines, schema management, partitioning, performance tuning; parquet/iceberg best practices.
• Testing & Monitoring: Unit/integration tests for data and models, contract tests for features, reproducible training; data drift/performance monitoring.
• Operational Mindset: Incident response for model services, SLOs, dashboards, runbooks; strong debugging across data, model, and infra layers.
• Soft Skills: Clear communication, collaborative mindset, and a bias to automate & document.

We comply with all applicable federal, state, and local laws. We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age national origin, religion, sexual orientation, gender identity, status as a veteran and basis of disability or any other federal, state or local protected class.