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The Role
Lead a data science team to design, build, and deploy ML models and scalable PySpark data pipelines on Azure. Oversee feature engineering, model lifecycle, monitoring, performance tuning, MLOps/CI-CD, and translate business needs into analytical solutions while presenting results to stakeholders.
Summary Generated by Built In
Data Science Lead (Predictive Analytics & ML):
Key Responsibilities
- Lead and mentor a team of data scientists and analysts; provide technical guidance and ensure high-quality deliverables.
- Design, develop, and optimize machine learning models (classification, regression, clustering, forecasting, etc.).
- Build and maintain big data processing pipelines using PySpark, Spark SQL, and distributed computing environments.
- Architect and deploy scalable ML solutions on Azure (Azure Databricks, Azure ML, ADLS, ADF).
- Oversee feature engineering, model lifecycle management, monitoring, and performance tuning.
- Collaborate with cross-functional teams to translate business needs into analytical solutions.
- Present insights, model outputs, and recommendations to technical and business stakeholders.
Required Skills
- 10+ years of hands-on experience in data science and ML development.
- Strong hands-on experience in leading complex analytical problem solving using traditional ML architecture (like supervised and unsupervised learning, time series and Deep learning) along with strong team leadership capabilities.
- Expertise in Python, PySpark, scikit‑learn, XGBoost, and related ML libraries.
- Strong experience with Azure data and ML services.
- Solid understanding of distributed computing and performance optimization using Spark.
- Proven ability to lead technical teams, conduct code reviews, mentor juniors, and manage project execution.
- Excellent communication, stakeholder management, and problem‑solving skills.
- Experience with MLOps practices and CI/CD pipelines.
Skills Required
- 10+ years hands-on experience in data science and ML development
- Proven experience leading and mentoring data science teams
- Designing and developing ML models (supervised, unsupervised, time series, deep learning)
- Expertise in Python
- Expertise in PySpark and Spark SQL
- Experience with scikit-learn and XGBoost
- Strong experience with Azure data and ML services
- Experience with Azure Databricks, Azure ML, ADLS, ADF
- Solid understanding of distributed computing and Spark performance optimization
- Experience with feature engineering, model lifecycle management, monitoring, and tuning
- Experience with MLOps practices and CI/CD pipelines
- Excellent communication and stakeholder management skills
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The Company
What We Do
Enable Data specializes in advanced data, application and cloud solutions working on cutting-edge projects driving innovation and transformation for our customers. We empower our customers to leverage modern solutions to deliver increased value across their business ecosystem. We help clients by providing consulting, managed project and staff augmentation services working with clients across various industries including healthcare, financial, media, insurance, and manufacturing.
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