Roles and Responsibilities
- Actively engage with internal technical and business teams to architect analytics-enabled digital products that deliver insights, predictions and optimization outcomes, at scale. Lead their ongoing evolution.
- Work alongside IT & engineering groups to leverage cutting edge technology (machine learning platforms, streaming analytics and real big data) to efficiently deploy data science algorithms, statistical models and analytical solutions into production
- Own responsibility for the Model Development Life Cycle. Guide your team to set up the environment, design CI/CD pipeline, test & optimize the code, manage model repository & deployments.
- Network and collaborate with a broad range of internal business stakeholders to define and deliver joint solutions.
- Create, maintain and document a robust set of metrics to monitor day-to-day bug detection and long-term performance tracking.
Required Technical Skills for the Candidate
- Experience in leading design/build of scalable data analytics products/solutions at an enterprise level
- Experience leading teams of machine learning engineers & data science personnel engaged in operationalizing high-performance analytical models using Python/R scripts, ML libraries, Jupyter notebooks.
- Good understanding of data science model deployments on CI/CD pipelines (specifically Kubeflow); and build/deployment of portable scalable machine learning workflows based on Docker containers
- Knowledge of Microsoft Azure Suite - Azure ML Studio, Azure Data Factory, Power BI/Power Apps
- Experience working with large datasets (several Gigs), using cloud hosted leading industry platforms or proprietary data science modeling / model deployment software
- Hands-on experience working with (any one of):AWS, Google Cloud, Microsoft Azure cloud orchestrations.
- Hands-on experience working with (any one of) database formats: Oracle, SQL Server, DB2,MySQL, Teradata
Education
- 10+ years of IT work experience with 5+ years in data engineering / data science roles
- Bachelors degree in Engineering, Math, Science, Statistics, Data Analytics, or Computer Science
- Excellent communication, people management and leadership skills