Lead Data Scientist

MUST HAVES: • Someone who can "Think Big Picture" to be hands-on and provide strategy/vision/guidance based on Data Science principles. If they are a Lead or Principal-level, they need to still be very hands-on with SQL and Python. • Strong SQL and Python experience is critical - will be expected to pass a coding test for the final round interview with Mastercard's end client. • Candidates in the data engineering/science space for at least 4-6 years with a total minimum work experience of at least 10 years for Lead/Principal levels. • Telecommunications or Financial Industry background (Telecommunications is HIGHLY preferred, but Financial industry is a backup option) • Experience translating data into business outcomes such as upsell, cross sell, and opportunity identification is critical. • Located in Basking Ridge, NJ - or - Irving, TX. Nice to Haves: • Role sits within Marketing, specifically in the Database Management team. Previous experience working with Marketing teams in prior projects would be a plus. Marketing or business development experience is preferred, though any gaps can be supplemented by Mastercard-provided consultants. • Strong preference for candidates who can operate at the front end of the analytics cycle (business-side), not just backend modeling. Marketing data experience is a strong differentiator, but broader commercial or customer analytics experience is acceptable. Responsible for leading the design and implementation of processes and layouts for complex, large-scale data sets used for modeling, data mining, and research purposes. Leads the design and build of large and complex data sets from spurious sources while thinking strategically about uses of data and how data use interacts with data design. Works with key stakeholders to generate hypotheses and create analytic models that answer impactful business questions. Develops resolutions to complex problems that require the use of creativity and makes decisions that impact projects and staff members. • Lead discussions with business clients on large-scale solution requests and begin determining data requirements and statistical methodologies. • Design and implement processes and layouts for complex, large-scale data sets used for modeling, data mining, and research purposes, implementing standard processes for peers to utilize. • Design and build large and complex data sets, from spurious sources while thinking strategically about uses of data and how data interacts with the design. • Develops end-to-end large-scale models, starting with complex distributed data processing, creating an analysis framework, developing custom complex visualization tools, and deploying web applications. • Develop and implement standardized models and procedures for other data scientists and teams to utilize for future projects and designs. • Design and implement large-scale statistical data quality procedures around new data sources. • • Develop and design data validation and updating procedures for teams to utilize. • Visualize and report data findings creatively in a variety of visual formats that appropriately provides insights to the organization. • Drive continuous improvement to the company's models, leveraging the latest industry knowledge and methods on AI and data science.