Senior Data Scientist - Python

Job Description :

We are seeking a highly skilled Senior Data Scientist to join our digital transformation team.

The ideal candidate will have hands-on experience with model development and a deep understanding of customer data ingestion, real-time data pipelines, and AI-driven marketing strategies.

You will be responsible for leveraging advanced analytics and machine learning to enable data-driven decision-making and hyper-personalized marketing at scale.

This role blends analytical rigor with innovative model building and maintenance, ensuring both operational excellence and strategic customer growth.

Key Responsibilities :

Model Development :

Daily Tasks :

- Design, prototype and iterate on ML models (segmentation, churn, personalization, propensity, campaign response) using Python and relevant ML frameworks.

- Ingest and preprocess customer data from batch and real-time sources; implement feature engineering and testing.

- Run experiments, evaluate metrics, and maintain model training pipelines to ensure reproducibility.

- Prepare model artifacts, documentation and handoffs for deployment teams (Martech, engineering).

- Participate in daily stand-ups with cross-functional teams and provide model status updates.

Measurable Expectations (targets) :

- Deliver 24 production-ready models per quarter aligned to prioritized business use cases.

- Achieve and maintain agreed performance thresholds (e.g., AUC/precision/recall or business KPIs) as defined with stakeholders prior to deployment.

- Document and version 100% of model code, data schemas and feature definitions for every production model.

- Automate training and deployment pipelines to reduce manual intervention by at least 50% versus baseline.

Additional Responsibilities :

- Translate model insights into actionable marketing strategies that enhance ROI and customer experience.

- Collaborate with Martech, digital, and commercial teams to operationalize models into platforms and workflows.

- Establish monitoring, retraining, and governance processes to ensure sustained model accuracy and business value.

Deep QA & Model Validation :

Daily Tasks :

- Execute end-to-end validation checks for models pre-deployment (data ingestion, schema, feature engineering, labels, performance metrics).

- Run automated and manual tests to detect data drift, label drift and performance regressions; triage anomalies and escalate as needed.

- Maintain validation notebooks, test suites and dashboards that capture validation outcomes and issues.

- Document validation findings, track remediation actions and confirm fixes before sign-off.

- Work with engineering/DevOps to ensure CI/CD and monitoring integrations are in place for each model.

Measurable Expectations (targets) :

- Validate 100% of models prior to production deployment and ensure documented sign-off for each release.

- Maintain validation coverage such that >95% of critical features and model pathways have automated tests and checks.

- Detect and surface drift within 2472 hours of onset and close high-priority validation incidents within 48 hours of identification.

- Produce validation reports/dashboards for stakeholders within 2 business days of model evaluation completion.

Other QA Responsibilities :

- Assess integration points (downstream outputs, GUI integration) to ensure model outputs are correctly consumed by products and campaigns.

- Support development of dashboards and reports to track validation progress, outcomes, and risks.

- Ensure every module is accurate, reliable, and business-aligned prior to deployment.

Required Qualifications :

- Master's degree in Data Science, Statistics, Computer Science, or related field.

- 4 to 6 years of experience in data science, model validation, and applied machine learning.

- Proven expertise in Python, SQL, and ML frameworks (scikit-learn, TensorFlow, PyTorch).

- Experience with QA and drift monitoring frameworks for ML models.

- Familiarity with big data platforms and cloud ecosystems such as Databricks, Spark, Snowflake, AWS, Azure, or GCP.

- Strong communication skills to convey complex insights to both technical and business stakeholders.

- Experience leading junior data scientists.

- Detail-oriented with a structured approach to QA, documentation, reporting, and dashboarding.