The Data Scientist role involves predictive analytics, NLP, and Deep Learning with a focus on feature engineering for structured and text data.
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
- 5–8 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.
Top Skills
Azure
Databricks
Pyspark
PyTorch
TensorFlow
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