Data Scientist

Job Title: Data Scientist
Start/End Dates: 6/15/2026 - 12/26/2026
Work Location: Raleigh CSC

Ideal Candidate Profile Summary:
Must have: Python & Pyspark experience
Preferred: AWS, Google Cloud Platform or other machine learning certifications
Preferred: XGBoos, Timeseries, Pytorch, or Tensorflow experience
Remote or On-site: This position is 4 days in office, 1 day remote per week, based at our corporate
headquarters in Raleigh, North Carolina (North Hills).
Role Summary
We are seeking an experienced Data Scientist with strong expertise in Data Science, machine learning
engineering with hands on experience in designing and deploying ML solutions in production. This role
focuses on building scalable ML solutions, productionizing models, and enabling robust ML platforms for
enterprise-grade deployments.
Key Responsibilities
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,
TensorFlow, and PyTorch.
Collaboration & Communication: Work closely with stakeholders to understand business
challenges and translate them into data science solutions and work in the end-to-end
solutioning. 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, Shapley values etc.
Productionize ML: Build repeatable paths from experimentation to deployment (batch,
streaming, and low-latency endpoints), including feature engineering, training, 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.
Required Qualifications
Education: Bachelor s degree in Computer Science, Information Technology, Data Science, or
related field.
Programming: 5+ years experience with Python (pandas, PySpark, scikit-learn; familiarity with
PyTorch/TensorFlow helpful), bash, experience with Docker.
ML Experimentation: 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).
ML Tooling: 5+ years experience with SageMaker (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 EventBridge/SQS/Kinesis.
Cloud Foundations: 3+ years experience with AWS/Azure/Google Cloud Platform on various services like ECR/ECS,
Lambda, API Gateway, S3, Glue/Athena/EMR, RDS/Aurora (PostgreSQL/MySQL), DynamoDB,
CloudWatch, IAM, VPC, WAF.
Snowflake Foundations: Warehouses, databases, schemas, stages, Snowflake SQL, RBAC, UDF,
Snowpark.
CI/CD: 3+ years hands-on experience with CodeBuild/Code Pipeline or GitHub Actions/GitLab;
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.
Additional Qualification:
Experience in retail/manufacturing is preferred.