Senior Data Scientist
Job Summary
Job Description
- 8+ years experience (or 5+ with doctorate)
- Experience implementing data science solutions
- Hands-on software engineering (Python, C++)
- MLOps/LLMOps experience
- Experience with LLMs, Computer Vision, Audio/Voice
- Customer interaction and business needs understanding
- Cloud deployment of large-scale AI solutions
- Excellent communication and collaboration skills
- Experience with LangChain, Semantic Kernel, Prompt Flow
11 skills required
Job Details
Overview
Are you a data scientist, and love what you do? Would you like to be a part of a global customer facing Team focused on solving complex, real-world business problems? Would you like to be a part of a community of technical leaders, highly specialised in their disciplines and working together as one to bring the best practices of engineering and architecture to world’s largest enterprise customers?
The Industry Solutions Delivery (ISD) Engineering & Architecture Group (EAG) is a global consulting and engineering organization that supports our most complex and leading-edge customer engagements. EAG enhances ISD’s technical capabilities, and partners with others to develop approaches, innovative solutions with engineering standards to set our sales and delivery teams up for success. We provide consistent high-quality customer experience through technical leadership and IP capture centered on delivery truth. We are committed to Responsible AI, and we help our customers and partners build ethical, transparent and trustworthy AI solutions.
We are hiring a Senior Data Scientist with experience in and passion for advanced statistical data analysis, and implementation of data science solutions in enterprises.
You'll work with high-impact professionals to solve complex problems for strategic customers and partners. You'll communicate trends and innovative solutions and collaborate cross-functionally within the Microsoft ecosystem, including product teams, research, security, solution strategy, industry excellence, and responsible AI.
Our team embraces a growth mindset and encourages diverse viewpoints. We value personal and cultural experiences and strive for excellence. We offer a flexible work environment to help you succeed in creating transformative and responsible AI solutions that positively impact billions worldwide.
Qualifications
Required/Minimum Qualifications
- Minimum of 8 years (for those with Bachelor’s Degree / Master’s Degree) or 5 years (for those with a Doctorate) of experience as a data scientist implementing data science solutions, with experience in implementing projects in one or more areas amongst: Computer Vision, LLMs, Audio/Voice data processing, and Reinforcement Learning.
- Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, Engineering or related field AND data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
- OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, Engineering or related field data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) or consulting experience.
- OR Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, Engineering or related field
- OR equivalent experience.
- Business level Japanese and English language skills.
Your success in this role will be achieved if you are comfortable interacting with external customers, are able to manage a complicated internal stakeholder group, and can demonstrate the following key knowledge, skills and experience:
- Hands-on software engineering experience (e.g. Python, C++).
- Proven skills and experience in a data science role.
- Familiarity with building and deploying largescale AI solutions into production within a cloud environment. Has experience in working with MLOps, LLMOps and frameworks like LangChain, Semantic Kernel and Prompt Flow.
- Experience of working with customers, directly and independently, in ensuring that the proposed solution addresses the business needs – through all stages from solutioning to deployment into production.
Responsibilities
Business Understanding and Impact:
Guides and ensures good practice on engagements that employ data science to align with business needs and deliver value. Provides thought leadership and guidance to software engineering and business stakeholders on how to best include artificial intelligence and machine learning in systems development.
Data Preparation and Understanding:
Leads data acquisition and understanding efforts for engineering projects using various tools and techniques that support the data science lifecycle.
Modeling and Statistical Analysis:
Develops and applies ML frameworks and best practices for scalable and ethical solutions.
Evaluation:
Oversees review of data analysis and modelling techniques. Ensures selected modelling techniques are appropriate and align with desired project outcomes. Decides on next steps (e.g., deployment, further iterations, new projects).
Industry and Research Knowledge/Opportunity Identification:
Provides feedback, drives improvement, and shares knowledge as a data science expert. Contributes to ongoing team learning by bringing relevant and leading edge concepts and approaches to the teams attention.
Coding and Debugging
Writes efficient, readable, extensible code from scratch that spans multiple features/solutions. Develops technical expertise in proper modeling, coding, and/or debugging techniques such as locating, isolating, and resolving errors and/or defects. Understands the causes of common defects and uses best practices in preventing them from occurring.
Business Management
Collaborates with end customer and Microsoft internal cross-functional stakeholders to understand business needs. Formulates a roadmap of project activity that leads to measurable improvement in business performance metrics over time.
Customer/Partner Orientation
Applies a customer-oriented focus by understanding customer needs and perspectives, validating customer perspectives, and focusing on broader customer organization/context. Promotes and ensures customer adoption by delivering model solutions and supporting relationships.
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