Data Scientist

Experience -12 Years

Product & Functional Skills
Looking for Sr. Data Scientist with deep expertise in machine learning, AI and a track record of developing production ML/AI solutions that are business impactful. As part of our team, you will be working side-by-side with high-impact engineers and strategic customers to solve complex problems. You will communicate trends and innovative solutions to stakeholders. You will work cross-functionally with several teams including engineering crews, product teams, and program management to deploy business solutions.

Role Specific Requirements
Responsibilities
Business Understanding and Impact
•        Learns and understands project objectives and requirements from a business perspective. Assists senior leads with the assessment of a project, including risks, contingencies, requirements, assumptions, and constraints. Contributes to the development of a project plan. Shares insights with stakeholders based on direct work.
Data Preparation and Understanding
•        Assists with initial data collection and familiarizes self with data in order to identify quality problems, discover insights into the data, and/or detect subsets to form hypotheses.
•        Understands which analysis techniques are appropriate for data and which key technologies and tools are necessary for data exploration (e.g., structured query language [SQL], Python).
•        Leverages data analysis knowledge to clean, transform, analyze, integrate, and organize data to the level required by the analysis techniques selected. Contributes to the description and exploration of data.
•        Develops foundational understanding of methodology and standard statistical options and when they should be used. Understands and follows ethics and privacy policies when collecting and preparing data.
•        Adheres to Microsoft's privacy policy related to collecting and preparing data. Identifies data integrity problems.
Modelling and Statistical Analysis
•        Learns and understands various modelling techniques used within the team (e.g., linear regression, multiple regression, decision-tree building, neural network generation, support machines, derivatives).
•        Runs model tools on prepared dataset to create one or more models, seeking guidance as needed.
•        Contributes to the research, identification, prototyping, and productizing of machine learning (ML)/artificial intelligence (AI) techniques and algorithms.
•        Collaborates with project managers and development engineers to design machine learning and artificial intelligence-driven features in the product.
Evaluation
•        Understands linkage between achieved model and business objectives. Assists with testing models on test applications and on real data or production data.