Specialist (Data Science)
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- 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.
- 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.
- 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
- Experience with database and model design and segmentation techniques
- Ownership
- Functional Skills
- Ideas presentation and Taking initiative
- Continuous Learning
- Customer Centricity
- Familiarity with Big Data Echo systems
- General knowledge regarding Data management and Governance
- Fluent in Spark coding