Data Scientist (ML & Statistics)

Job highlights

• Experience with cloud data platforms such as Snowflake,BigQuery,Redshift,Databricks or Vertica,and with large-scale data processing (Spark is a plus).
• Experience with causal inference or uplift modeling is preferred.

Crunch Numbers. Build Models. Shape Strategy. Create Impact!

Role Overview

We are seeking a Data Scientist who can turn statistical ideas into models that loyalty clients rely on every day. You will build predictive and causal models on member-level data from programs with millions of members, run them reliably in production, and explain their impact to client stakeholders.

What you will OWN

Predictive Statistical Modeling (technical):

Design, train and validate models for churn,propensity, next-best-offer, lifetime value, tier migration and anomaly or fraud detection.Choose methods on evidence: regression and GLMs, survival analysis, gradient boosting,probabilistic and Bayesian models, time-series forecasting, and deep learning where itearns its complexity.

Experimentation Causal Inference (technical):

Design A/B tests and holdouts forcampaigns, offers and program changes, including power analysis and sample sizing.Apply uplift modeling and causal methods (diff-in-diff, propensity matching) to measurethe true incremental impact of loyalty initiatives when randomization is not possible.

Feature Engineering Data Pipelines (technical):

Build and maintain reusable featuresets from large transactional and behavioral datasets. Work with Data Engineering ondata quality, lineage and scalable processing in the data warehouse and cloud platform.

Client Engagement Insight Delivery (business):

Partner with client stakeholders toframe business questions as modeling problems, define success metrics, and presentresults and recommendations in executive reviews. Translate model outputs into clearactions for marketing, product and finance teams, and document models, assumptionsand limitations.

To be successful in this role, you should have:

• Bachelors or Masters degree in Statistics, Mathematics, Computer Science,Engineering, Data Science or a related quantitative field. An MBA or a postgraduatedegree in Analytics is an added advantage.
• 1-4 years of experience building and deploying ML or statistical models in production,ideally in a SaaS, consumer or loyalty/CRM setting.
• Strong Python (pandas, NumPy, scikit-learn, XGBoost/LightGBM, statsmodels; PyTorchor TensorFlow is a plus) and strong SQL.
• Solid statistical foundations: hypothesis testing, regression, probability, Bayesianmethods, time-series, survival analysis and experimental design.
• Experience with cloud data platforms such as Snowflake, BigQuery, Redshift,Databricks or Vertica, and with large-scale data processing (Spark is a plus).
• Working knowledge of a BI tool (Tableau, Power BI, Looker or similar) to share modeloutputs.
• Experience with causal inference or uplift modeling is preferred.
• Exposure to enterprise SaaS products or customer loyalty programs is preferred.
• Strong communication skills: able to explain model logic, trade-offs and uncertainty tonon-technical stakeholders.
• Understanding of Agile methodologies and comfort working in a fast-pacedenvironment.

Key Competencies

• Statistical Modeling Machine Learning
• Experimentation Causal Thinking
• Production-Grade Engineering MLOps
• Problem Solving Root Cause Analysis
• Business Problem Framing
• Stakeholder Communication Data Storytelling
• Attention to Detail Reproducibility
• Collaboration Ownership

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.