Head of Data Science

Job Title:

Location:

Department:

Employment Type:

About the Role

Senior Data Scientist with deep experience on the AWS stack

You will partner closely with product, engineering, and business stakeholders to deliver measurable impact using machine learning, experimentation, and advanced analytics on top of AWS data and ML services.

Key Responsibilities

1. Problem Definition & Stakeholder Collaboration

• Work with business, product, and engineering teams to translate ambiguous problems into clear data science use cases and success metrics.
• Define hypotheses, experimentation strategies, and measurable outcomes for ML and analytics initiatives.

2. Data & Feature Engineering (AWS-native)

• Design and build 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.

3. Modeling & Analytics

• Build, 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.

4. MLOps & Deployment (AWS SageMaker)

• Productionize 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.

5. Leadership & Mentoring

• Provide 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 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 Formation

• ML:

  SageMaker (training jobs, endpoints, pipelines, model registry)

• Orchestration/Integration:

  Lambda, Step Functions, EventBridge, API Gateway

• Solid 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

  .