Overview
On Site
Depends on Experience
Full Time
Skills
Machine Learning (ML)
Artificial Intelligence
Mentorship
Microsoft Azure
Job Details
We are looking for a highly experienced Data Science Practice Lead to lead the strategy, design, and implementation of advanced analytics and machine learning solutions. The ideal candidate will bring deep technical expertise, leadership experience, and a business-oriented mindset to drive data science initiatives that deliver measurable impact.
Key Responsibilities:
- Lead the end-to-end architecture and design of scalable, production-grade data science and machine learning platforms.
- Collaborate with business stakeholders to translate complex business problems into analytical models and solutions.
- Architect ML pipelines, including data preprocessing, model training, testing, deployment, and monitoring using MLOps best practices.
- Guide the evaluation and selection of algorithms, models, and tools appropriate to the business use case.
- Implement model versioning, governance, and lifecycle management strategies to ensure reliability and compliance.
- Drive cloud-based ML architecture (AWS, Azure, or Google Cloud Platform), integrating with data lakes, warehouses, and APIs.
- Collaborate with data engineers, analysts, and product teams to operationalize models at scale.
- Mentor junior data scientists and serve as a technical advisor across the enterprise on AI/ML initiatives.
- Stay up to date with emerging trends in machine learning, AI, and data engineering.
Technical Skills:
- Bachelor's or Master s in Computer Science, Data Science, Statistics, Mathematics, or related field (Ph.D. preferred).
- 12+ years of experience in data science, advanced analytics, or related roles, with at least 3+ years in an architectural or lead role.
- Proven expertise in machine learning, deep learning, and statistical modeling.
- Hands-on experience with Python, R, SQL, and ML libraries like Scikit-learn, TensorFlow, PyTorch, XGBoost.
- Experience in cloud platforms (AWS SageMaker, Azure ML, Google Cloud Platform Vertex AI) and big data tools (Spark, Hadoop, Hive, Kafka).
- Strong understanding of data governance, security, and compliance in an enterprise context.
- Knowledge of MLOps frameworks such as MLflow, Kubeflow, or Airflow.
- Excellent communication, presentation, and stakeholder management skills.
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