Data Scientist

at  Microsoft

Are you an experienced Data Scientist, and you love what you do? Would you like to be a part of a global customer facing Team focussed on solving complex, real-world business
problems? Would you like to be a part of a worldclass community of technical leaders, highly specialised in their disciplines and working together as one to bring the best practices of
Artificial Intelligence, Machine learning, Engineering and Architecture to world’s largest enterprise customers?
The Industry Solutions Delivery (ISD) Engineering & Architecture Group (EAG) is a global engineering consulting organisation that supports our most complex and leading edge customer engagements in improving their business performance with the power of Data & AI. EAG develops approaches, innovative solutions, and engineering standards to set our delivery teams and customers up for long-lasting success. We are committed to Responsible AI, and we help our customers build and operate ethical, transparent and trustworthy AI solutions.
We are hiring a Data Scientist with deep experience in advanced statistical data analysis, machine learning and artificial intelligence.
Our team embraces a continuous learning and growth mindset, encourages diverse viewpoints and relentless collaboration. 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 economy and people worldwide.
Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

REQUIRED/MINIMUM QUALIFICATIONS

Bachelor’s degree in Data Science, Computer Science, Statistics, Operations Research, or a related field, with proven years of data science experience in business context.
Be able to work independently, solve complex data science problems, design and code maintainable and scalable solutions, and effectively apply data science to business challenges.

ADDITIONAL OR PREFERRED QUALIFICATIONS

• Candidates must have a Bachelor’s degree in Data Science, Computer Science, Statistics, Operations Research, or a related field, with sufficient years of data science experience. A Master’s or Ph.D. in a relevant field is preferred.
• Hands-on software engineering experience (e.g. Python, Scikit, PyTorch,C++) with main established data science frameworks
• Proven skills and experience in a data science role
• Familiarity with building and deploying largescale AI solutions into production within a cloud environment
• Experience dealing with internal and external stakeholders on large, complex projects.
  Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations

• Leverage data science and business domain knowledge to improve business performance, evaluate project plans, communicate business goals, and share insights with stakeholders.
• Acquire, prepare, and explore data through querying, visualisation, reporting techniques, and collaboration with other teams, ensuring data integrity.
• Apply machine learning and statistical analysis to develop models, train, optimise, and evaluate them, and communicate findings to stakeholders.
• Test, review, and improve models by analysing performance, incorporating feedback, and contributing to the review process.
• Write and debug efficient and scalable code while collaborating with engineering teams and integrate data models into customer systems.