100% Remote -- Data Scientist (Gen AI)

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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.

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.

• Modeling and Statistical Analysis

• Learns and understands various modeling 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.

• Analyzes model performance. Incorporates implicit and explicit customer feedback into model evaluation.

• Conducts review of data analysis and modeling techniques to determine factors that may have been overlooked or need to be reexamined. Contributes to the summary of the review process.

• Industry and Research Knowledge/Opportunity Identification

• Learns and understands the current state of the industry, including knowledge of tools, techniques, strategies, and processes that can be utilized to improve process efficiency and performance.

• Maintains knowledge of current trends within the discipline. Attends internal research conferences and participates in on-hands training, when appropriate. Actively contributes to the body of thought leadership and intellectual property (IP) best practices.

• Coding and Debugging

• Writes readable code for a specific feature, enhancement and/or model, seeking guidance when needed. Contributes to the development, testing, and implementation of changes to optimize code and improve the reliability of systems/solutions.

• Develops an understanding of proper debugging techniques such as locating, isolating, and resolving errors and/or defects.

• Understands known issues and learns from senior developers/team members. Develops foundational understanding of Agile methodology and when they should be used. Contributes to documentation for productionalisation.

• Business Management

• Develops understanding of data structures and their relationship to Microsoft's customer business. Observes senior engineers and learns best practices in identifying growth opportunities, understanding strategy goals, customer- and product-strategy goals, and exploring opportunities for machine learning (ML) application, seeking guidance when needed. Understands business goals of the customer, per engagement basis.

• Customer/Partner Orientation

• Leverages understanding of data science and business to examine projects through a customer-oriented focus. Manages customer expectations regarding project/product progress and timeline. Takes responsibility to enhance customer excellence. Assists and learns from senior team members to interpret results, develop insights, and communicate results to customers. Possesses basic understanding about model accuracies dependency on data quality and able to articulate it in customer discussions.