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.(ref : hirist.tech)
