Manager - Data Scientist

Project Leadership and Management:

· Lead complex data science initiatives utilizing Databricks, Dataiku, and modern AI/ML frameworks for end-to-end solution development

· Establish and maintain data quality frameworks and metrics across all stages of model development

· Design and implement data validation pipelines and quality control mechanisms for both structured and unstructured data

Strategic Development:

· Develop and deploy advanced machine learning models, including deep learning and generative AI solutions

· Design and implement automated data quality monitoring systems and anomaly detection frameworks

· Create and maintain MLOps pipelines for model deployment, monitoring, and maintenance

Team Mentoring and Development:

· Lead and mentor a team of data scientists and analysts, fostering a culture of technical excellence and continuous learning

· Develop and implement training programs to enhance team capabilities in emerging technologies and methodologies

· Establish performance metrics and career development pathways for team members

· Drive knowledge sharing initiatives and best practices across the organization

· Provide technical guidance and code reviews to ensure high-quality deliverables

Data Quality and Governance:

· Establish data quality standards and best practices for data collection, preprocessing, and feature engineering

· Implement data validation frameworks and quality checks throughout the ML pipeline

· Design and maintain data documentation systems and metadata management processes

· Lead initiatives for data quality improvement and standardization across projects

Technical Implementation:

· Design, develop and deploy end-to-end AI/ML solutions using modern frameworks including TensorFlow, PyTorch, scikit-learn, XGBoost for machine learning, BERT and GPT for NLP, and OpenCV for computer vision applications

· Architect and implement robust data processing pipelines leveraging enterprise platforms like Databricks, Apache Spark, Pandas for data transformation, Dataiku and Apache Airflow for ETL/ELT processes, and DVC for data version control

· Establish and maintain production-grade MLOps practices including model deployment, monitoring, A/B testing, and continuous integration/deployment pipelines

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, Naïve-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