Data Science Manager

Overview

​We are hiring a Data Science Manager with deep experience in developing data driven solutions and expertise in conventional and state of the art Machine Learning (ML) and AI technologies. As part of our team, you will be working side-by-side with high-impact data scientists and strategic customers to solve complex problems. You will communicate trends and innovative AI solutions to stakeholders. You will work cross-functionally with several teams including engineering crews, product teams, and program management to deploy business solutions.

Industry Solutions Engineering (ISE) is part of Microsoft Industry Solutions, a global organization of over 16,000 strategic sellers, industry experts, elite engineers, and world-class architects, consultants, and delivery experts who work together to bring Microsoft’s mission of empowerment – and cutting-edge technology - to life for the world’s most influential customers.   

The ISE team works directly with customers looking to leverage the latest technologies to address their toughest challenges. We develop solutions side-by-side with our customers through collaborative innovation to solve their challenges. This work involves the development of broadly applicable, high-impact solution patterns and open-source software assets that contribute to the Microsoft platform. In this role, you will be working with engineers from your team and our customers’ teams to apply your skills, perspectives, and creativity to grow as engineers and help solve our customer challenges.   

Our team prides itself on embracing a growth mindset, inspiring excellence, and encouraging everyone to share their unique viewpoints and be their authentic selves. Join us and help create life-changing innovations that impact billions around the world. 

   Watch this video to learn more about who we are and what we do: https://aka.ms/csevideo.   

Responsibilities

People Management

• Managers deliver success through empowerment and accountability by modeling, coaching, and caring.
• Model - Live our culture; Embody our values; Practice our leadership principles.
• Coach - Define team objectives and outcomes; Enable success across boundaries; Help the team adapt and learn.
• Care - Attract and retain great people; Know each individual’s capabilities and aspirations; Invest in the growth of others.

Business Understanding and Technical Oversight

• Leads data-science projects or teams to align with business needs and deliver value.
• Defines and communicates the technical direction and strategy for the team and the project. This involves understanding the project goals, industry trends, and technical delivery context, guiding decision-making, ensuring a focused approach, and alignment with business objectives.
• Provide technical guidance, and suggest best practice, and drive team agreements related to best practices. Offers support to team members as and when required.
• Ensures the quality of the project deliverables is of the required standard. This involves creating clarity on expected standards, and overseeing the implementation to ensure they are adhered to.

Data Preparation and Understanding

• Performs, and oversees, extensive exploratory data analysis on large and complex datasets to identify patterns, trends, and relationships, and ultimately generate high quality data and relevant hypotheses. This process involves applying statistical techniques such as hypothesis testing and regression analysis, as well as performing data cleaning, preprocessing, and addressing missing data, outliers, and anomalies to ensure high data quality and reliability.

Experimental design and hypothesis testing

• Develops and applies ML/AI techniques and best practices for reliable, scalable, and ethical data driven solutions. This involves devising experimental methodologies and implementing hypothesis testing to investigate the viability and credibility of novel concepts and modifications.

Evaluation

• Collaborate with stakeholders to understand the specific objectives and goals of project, and aid in designing a formal way of evaluating the systems’ performance against these. This may involve identifying key performance indicators (KPIs) and metrics that align with the organization’s objectives and identifying associations between these and the classical performance indications for models used in the system.

Industry and Research Knowledge/Opportunity Identification

• Provides feedback, drives improvement, and shares knowledge as an Industry data science expert.

Coding and Debugging

• Writes, reviews, and debugs code for complex projects and leads solution development.

Business Management

• Leads data-science partnerships and IP improvement.

Customer/Partner Orientation

• Provides customer-oriented insights and solutions by understanding the business, product, data, and customer perspective.

Responsible AI

• Adheres to Microsoft’s AI Customer Commitment