Specialist (Data Science)

• End-to-end ownership of models — from framing new projects and data work through deployment, monitoring, and retraining, as well as owning models already in production.
• Scoping and kicking off new projects — problem framing, data exploration, and initial modeling — not just maintaining what's already shipped.
• Code review for the Data Science team: setting and enforcing standards for readability, testing, and maintainability across everyone's PRs, not just your own.
• CI/CD for model and pipeline code — build, test, and deployment pipelines that let the team ship changes safely and often.
• Production reliability — monitoring, alerting, and incident response for models you and your squad own once they're live.
• Mentoring less senior team members on both modeling judgment and software engineering practice.

Responsibilities

• End-to-end ownership of models — from framing new projects and data work through deployment, monitoring, and retraining, as well as owning models already in production.
• Scoping and kicking off new projects — problem framing, data exploration, and initial modeling — not just maintaining what's already shipped.
• Code review for the Data Science team: setting and enforcing standards for readability, testing, and maintainability across everyone's PRs, not just your own.
• CI/CD for model and pipeline code — build, test, and deployment pipelines that let the team ship changes safely and often.
• Production reliability — monitoring, alerting, and incident response for models you and your squad own once they're live.
• Mentoring less senior team members on both modeling judgment and software engineering practice.

Qualifications

• Bachelor's degree in Computer Science, Statistics, Engineering, or a related quantitative field required; Master's a plus

• 3-5 years of proven experience as a Data Analyst or Data Scientist and 

   

• Technical Requirements:
  • Strong computer science fundamentals: clean, well-tested, version-controlled code — not notebook-only work.
  • Hands-on CI/CD experience: building and maintaining pipelines (e.g. GitHub Actions, GitLab CI, Jenkins, or similar).
  • Real MLOps experience: model packaging, deployment, monitoring, and retraining in production — not just training a model and handing it off.
  • A versatile, generalist data science background: comfortable moving across problem types and owning a project from problem definition through modeling and evaluation, rather than specializing narrowly in one domain.
  • Core ML & statistics toolkit: regression and classification, tree-based ensembles (XGBoost/LightGBM/Random Forest), scorecard/GLM-style modeling, feature engineering, cross-validation, and rigorous model evaluation (AUC, KS, precision/recall, calibration).
  • Time-series and forecasting methods: classical approaches (ARIMA/exponential smoothing) as well as ML-based forecasting, with the judgment to choose the right method for the data and business question.
  • Strong data wrangling and cleaning skills: comfortable working with messy, incomplete, or inconsistent real-world data, building robust preprocessing pipelines, and validating data quality before it reaches a model.
  • Comfortable giving and receiving code review, and setting a bar for the rest of the team, not just meeting it.
  • Experience with a modern ML stack (Python, SQL, common ML/DL frameworks) — specific cloud provider experience is not required.