Senior Data Scientist Python

Job description

<p><p><b>Job Description : </b><br/><br/>We are seeking a highly skilled Senior Data Scientist to join our digital transformation team.<br/><br/>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.<br/><br/>You will be responsible for leveraging advanced analytics and machine learning to enable data-driven decision-making and hyper-personalized marketing at scale.<br/><br/>This role blends analytical rigor with innovative model building and maintenance, ensuring both operational excellence and strategic customer growth.<br/><br/><b>Key Responsibilities : </b><br/><br/><b>Model Development : </b><br/><br/><b>Daily Tasks : </b><br/><br/>- Design, prototype and iterate on ML models (segmentation, churn, personalization, propensity, campaign response) using Python and relevant ML frameworks.<br/><br/></p><p>- Ingest and preprocess customer data from batch and real-time sources; implement feature engineering and testing.<br/><br/></p><p>- Run experiments, evaluate metrics, and maintain model training pipelines to ensure reproducibility.<br/><br/></p><p>- Prepare model artifacts, documentation and handoffs for deployment teams (Martech, engineering).<br/><br/></p><p>- Participate in daily stand-ups with cross-functional teams and provide model status updates.<br/><br/><b>Measurable Expectations (targets) : </b><br/><br/>- Deliver 24 production-ready models per quarter aligned to prioritized business use cases.<br/><br/></p><p>- Achieve and maintain agreed performance thresholds (e.g., AUC/precision/recall or business KPIs) as defined with stakeholders prior to deployment.<br/><br/></p><p>- Document and version 100% of model code, data schemas and feature definitions for every production model.<br/><br/></p><p>- Automate training and deployment pipelines to reduce manual intervention by at least 50% versus baseline.<br/><br/><b>Additional Responsibilities : </b><br/><br/>- Translate model insights into actionable marketing strategies that enhance ROI and customer experience.<br/><br/></p><p>- Collaborate with Martech, digital, and commercial teams to operationalize models into platforms and workflows.<br/><br/></p><p>- Establish monitoring, retraining, and governance processes to ensure sustained model accuracy and business value.<br/><br/><b>Deep QA & Model Validation : </b><br/><br/><b>Daily Tasks : </b><br/><br/>- Execute end-to-end validation checks for models pre-deployment (data ingestion, schema, feature engineering, labels, performance metrics).<br/><br/></p><p>- Run automated and manual tests to detect data drift, label drift and performance regressions; triage anomalies and escalate as needed.<br/><br/></p><p>- Maintain validation notebooks, test suites and dashboards that capture validation outcomes and issues.<br/><br/></p><p>- Document validation findings, track remediation actions and confirm fixes before sign-off.<br/><br/></p><p>- Work with engineering/DevOps to ensure CI/CD and monitoring integrations are in place for each model.<br/><br/><b>Measurable Expectations (targets) : </b><br/><br/>- Validate 100% of models prior to production deployment and ensure documented sign-off for each release.<br/><br/></p><p>- Maintain validation coverage such that &gt;95% of critical features and model pathways have automated tests and checks.<br/><br/></p><p>- Detect and surface drift within 2472 hours of onset and close high-priority validation incidents within 48 hours of identification.<br/><br/></p><p>- Produce validation reports/dashboards for stakeholders within 2 business days of model evaluation completion.<br/><br/><b>Other QA Responsibilities : </b><br/><br/>- Assess integration points (downstream outputs, GUI integration) to ensure model outputs are correctly consumed by products and campaigns.<br/><br/></p><p>- Support development of dashboards and reports to track validation progress, outcomes, and risks.<br/><br/></p><p>- Ensure every module is accurate, reliable, and business-aligned prior to deployment.<br/><br/><b>Required Qualifications : </b><br/><br/>- Master's degree in Data Science, Statistics, Computer Science, or related field.<br/><br/></p><p>- 4 to 6 years of experience in data science, model validation, and applied machine learning.<br/><br/></p><p>- Proven expertise in Python, SQL, and ML frameworks (scikit-learn, TensorFlow, PyTorch).<br/><br/></p><p>- Experience with QA and drift monitoring frameworks for ML models.<br/><br/></p><p>- Familiarity with big data platforms and cloud ecosystems such as Databricks, Spark, Snowflake, AWS, Azure, or GCP.<br/><br/></p><p>- Strong communication skills to convey complex insights to both technical and business stakeholders.<br/><br/></p><p>- Experience leading junior data scientists.<br/><br/></p><p>- Detail-oriented with a structured approach to QA, documentation, reporting, and dashboarding.<br/></p><br/></p> (ref:hirist.tech)

Required Skill Profession

Mathematical Science Occupations