DT-US Product Engineering - Data Scientist Manager 

We are seeking an exceptional Data Scientist who combines deep expertise in AI/ML with a strong focus on data quality and advanced analytics. This role requires a proven track record in developing production-grade machine learning solutions, implementing robust data quality frameworks, and leveraging cutting-edge analytical tools to drive business transformation through data-driven insights.

Work you will do

The Data Scientist will be responsible for developing and implementing end-to-end AI/ML solutions while ensuring data quality excellence across all stages of the data lifecycle. This role requires extensive experience in modern data science platforms, AI frameworks, and analytical tools, with a focus on scalable and production-ready implementations.

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