Chief Data Strategy Officer

Lead Data Science ProfessionalWe are seeking an experienced Lead Data Science Professional to spearhead our data science initiatives and drive business growth through actionable insights.About the RoleThis is a full-time position that involves working closely with cross-functional teams to develop and deploy machine learning solutions across various business domains. The successful candidate will have a strong background in data science, excellent technical skills, and the ability to communicate complex ideas effectively.Key Responsibilities:Problem Definition & Stakeholder CollaborationWork with business stakeholders to identify opportunities for data-driven decision-making and define clear objectives and success metrics.Develop and execute strategies for experimentation and analysis to inform business decisions.Data & Feature EngineeringDesign and implement robust data pipelines using AWS services such as S3, Glue, Athena, Redshift, EMR, Lambda, Step Functions.Develop scalable feature stores and reusable data assets for multiple ML use cases.Ensure data quality, observability, and governance in collaboration with data engineering teams.Modeling & AnalyticsBuild, train, and optimize models for use cases such as prediction, recommendation, forecasting, personalization, segmentation, anomaly detection, etc.Use Python and standard ML libraries (e.g., scikit-learn, XGBoost, PyTorch/TensorFlow) for experimentation and prototyping.Design and run A/B tests, holdout experiments, and causal analyses to measure impact.MLOps & DeploymentProductionize models using Amazon SageMaker (training, tuning, endpoints, pipelines, model registry).Implement CI/CD for ML, monitoring and alerting for model drift, data drift, and performance degradation.Optimize cost and performance of deployed models and pipelines.Leadership & MentoringProvide technical leadership on projects, setting standards for experimentation, documentation, and code quality.Mentor junior data scientists and analysts; contribute to best practices, templates, and internal tooling.Advocate for data-driven decision-making across the organization.Required Skills and Qualifications:7+ years of hands-on experience in data science or applied machine learning roles.Strong proficiency in Python and ML/data libraries (pandas, numpy, scikit-learn, XGBoost, PyTorch/TensorFlow).Demonstrated experience building and deploying ML solutions on AWS, including:Data: S3, Glue, Athena, Redshift, EMR / AWS Lake FormationML: SageMaker (training jobs, endpoints, pipelines, model registry)Orchestration/Integration: Lambda, Step Functions, EventBridge, API GatewaySolid understanding of statistics, experimental design, and causal inference (A/B testing, hypothesis testing, confidence intervals, etc.).Proven track record of delivering ML solutions into production with measurable business impact.Strong SQL skills and comfort working with large-scale datasets in data lake / data warehouse environments.Excellent communication skills—able to explain complex topics to both technical and non-technical stakeholders.Preferred Qualifications:Experience with streaming data / real-time ML using Kinesis, Kafka/MSK, or similar.Experience with feature stores (SageMaker Feature Store or equivalent) and ML observability tools.Domain experience in one or more areas such as marketing analytics, customer personalization, fraud/risk, pricing, demand forecasting, or operations optimization.Familiarity with MLOps best practices (Git-based workflows, CI/CD, model versioning, monitoring).AWS certifications such as AWS Certified Machine Learning – Specialty or AWS Data Analytics – Specialty.