About the role
Lead Data Scientist
Overview: As a Lead Data Scientist, you will be crucial to the Data Science team. You will support analytics technical requirements, establish best practices in use case development, deployment, scaling, and maintenance of data science projects, and foster partnerships with AI vendors and startups. Your responsibilities include developing and deploying machine learning and statistical models to address complex operational challenges, building scalable data products, and collaborating with stakeholders globally
Key Responsibilities:
Collaborate with stakeholders to translate their needs into predictive and prescriptive analytics solutions.
Work closely with necessary BUs to build data pipelines for scalable and production-ready analytics solutions.
Perform exploratory data analysis and create compelling visualizations using complex, high-dimensional datasets.
Identify appropriate machine learning or statistical approaches for various problems and train models for optimal performance, including hyperparameter tuning.
Deploy models into production with the assistance of Product Engineers.
Validate and monitor models to ensure consistent performance and address any performance degradation.
Lead the development of technical and analytics capabilities within the team, staying current on best practices and new modeling techniques.
Manage and mentor a team of data scientists, overseeing workloads, establishing common modeling standards and practices, and providing expertise.
Communicate the value of analytics to Fab leadership and business partners to increase awareness and adoption of analytics solutions.
Drive value generation from analytics solutions.
Additional Responsibilities:
Conduct all activities safely and responsibly, supporting all Environmental, Health, Safety & Security requirements and programs.
Required Qualifications:
Bachelors degree in a quantitative field such as Statistics, Mathematics, Computer Science, Operations Research, Engineering, Physics, Chemistry, Biostatistics, Economics, Data Science, or a related discipline.
At least 10 years of experience in the analytics field, with a proven track record of innovation.
A minimum of 5 years of experience in leading technical teams.
Proficiency in various data science techniques such as Forecasting, Recommendation Systems, Logistics Optimization, Churn Analysis, Segmentation Analysis, and Deep Learning.
Experience creating business models using tools like R, Python, SAS, SPSS, or STATA (data visualization experience is a plus).
Experience in optimization and cloud environments, leading interdisciplinary teams through scaling and productionizing recommendation engines.
Ability to implement business-focused advanced analyses that capture industry-specific nuances.
Strong communication skills to engage with both technical and non-technical audiences.
Demonstrated leadership skills to mentor and guide team members for timely and excellent project delivery.
Product mindset with extensive experience working in Agile teams.
Ability to build trust and rapport within the team and with senior leadership.
Flexibility to work with ambiguous problems and unstructured data.
Strong bias for action with a proven ability to iteratively and quickly deliver incremental impact and value.
Preferred Qualifications:
Masters degree in Data Science, Computer Science, or another quantitative field.
Originally posted on Himalayas
