Data Scientist/Manager (10-14 yrs)

Technical Expertise Requirements :

Must Have :

- Enterprise AI/ML Platforms : Demonstrate mastery of Databricks for large-scale processing, with proven ability to architect solutions at scale

- Programming & Analysis : Advanced Python (NumPy, Pandas, scikit-learn), SQL, PySpark with production level expertise

- Machine Learning : Deep expertise in TensorFlow or PyTorch, and scikit-learn with proven implementation experience

- Big Data Technologies : Advanced knowledge of Apache Spark, Databricks, and distributed computing architectures

- Cloud Platforms: Strong experience with at least one major cloud platform (AWS/Azure/GCP) and their ML services (SageMaker/Azure ML/Vertex AI)

- Data Processing & Analytics : Extensive experience with enterprise-grade data processing tools and ETL pipelines

- MLOps & Infrastructure : Proven experience in model deployment, monitoring, and maintaining production ML systems

- Data Quality : Experience implementing comprehensive data quality frameworks and validation systems

- Version Control & Collaboration : Strong proficiency with Git, JIRA, and collaborative development practices

- Database Systems : Expert-level knowledge of both SQL and NoSQL databases for large-scale data management

- Visualization Tools : Tableau, Power BI, Plotly, Seaborn

- Large Language Models : Experience with GPT, BERT, LLaMA, and fine-tuning methodologies

Good to Have :

- Additional Programming : R, Julia

- Additional Big Data : Hadoop, Hive, Apache Kafka

- Multi-Cloud : Experience across AWS, Azure, and GCP platforms

- Advanced Analytics : Dataiku, H2O.ai

- Additional MLOps : MLflow, Kubeflow, DVC (Data Version Control)

- Data Quality & Validation : Great Expectations, Deequ, Apache Griffin

- Business Intelligence : SAP HANA, SAP Business Objects, SAP BW

- Specialized Databases : Cassandra, MongoDB, Neo4j

- Container Orchestration : Kubernetes, Docker

- Additional Collaboration Tools : Confluence, BitBucket

Education :

- Advanced degree in quantitative discipline (Statistics, Math, Computer Science, Engineering) or relevant experience.

Qualifications :

- 10-13 years of experience with data mining, statistical modeling tools and underlying algorithms.

- 5+ years of experience with data analysis software for large scale analysis of structured and unstructured data.

- Proven track record of leading and delivering large-scale machine learning projects, including production model deployment, data quality framework implementation and experience with very large datasets to create data-driven insights thru predictive and prescriptive analytic models.

- Extensive knowledge of supervised and unsupervised analytic modeling techniques such as linear and logistic regression, support vector machines, decision trees / random forests, Na- ve-Bayesian, neural networks, association rules, text mining, and k-nearest neighbors among other clustering models.

- Extensive experience with deep learning frameworks, automated ML platforms, data processing tools (Databricks Delta Lake, Apache Spark), analytics platforms (Tableau, Power BI), and major cloud providers (AWS, Azure, GCP)

- Experience architecting and implementing enterprise-grade solutions using cloud-native ML services while

ensuring cost optimization and performance efficiency

- Strong track record of team leadership, stakeholder management, and driving technical excellence across multiple concurrent projects

- Expert-level proficiency in Python, R, and SQL, with deep understanding of statistical analysis, hypothesis testing, feature engineering, model evaluation, and validation techniques in production environments

- Demonstrated leadership experience in implementing MLOps practices, including model monitoring, A/B testing frameworks, and maintaining production ML systems at scale.

- Working knowledge of supervised and unsupervised learning techniques, such as Regression/Generalized Linear Models, decision tree analysis, boosting and bagging, Principal Components Analysis, and clustering methods.

- Strong oral and written communication skills, including presentation skills