Data Scientist is a role to build world class Data Science/ business intelligence, analytics, and reporting systems for ADS teams.
The Data Scientist– ADS Technology is accountable for the establishing performance measures and analytics for ADS teams. Facilitate creation of KPIs and KPMs, defined standards on measurement, reporting, and reviewing them to keep performance of ADS operations teams on track. (S)He should thrive and have demonstrated success in an environment which offers ambiguously defined problems, big challenges, and quick changes. (S)He will be expected to balance detailed execution with speed and possess solid collaborative skills. (S)He will be working in a fast-paced environment where every day brings new challenges and new opportunities. (S)He should have excellent business and communication skills and be able to work with business owners to develop and define solutions. This position involves regular communication with senior management on project status and risks. Cross-team coordination, project management, and executive presentation skills are essential.
The successful candidate will be a recognized expert for their analytical and leadership abilities.
· Apply Statistical and Machine Learning methods to specific business problems and data
· Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc.
· Collaborate with colleagues from multidisciplinary science, engineering and business backgrounds.
· Work with engineers to develop efficient data querying and modeling infrastructure
· Manage your own process: identify and execute on high impact projects, triage external requests, and make sure you bring projects to conclusion in time for the results to be useful
· Communicate proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions
· Work with teams in ADS across verticals, define success measures at team level and required KPI and KPMs to be tracked.
· Establish reporting standards by role, region, and vertical. Apply data engineering to deliver automated solutions, where possible.
· Assess performance of KPIs and KPMs; surface exceptions to the respective teams. Setup and execute processes to enable operations to drive corrective actions as necessary.
· Setup and execute processes of back-testing to verify the corrective action effectiveness and report
· Setup and execute processes for creation and distribution canned analysis reports where automated solutions do not exist.
· Setup and execute processes for performing exploratory data analysis to unearth insight or patterns that can be used to drive operational efficiency, further control cost per transaction of the operations teams.
· Work closely with Workflow, Data Engineering, and Software Engineering teams to drive initiatives on Operational efficiency.