Data Scientist 2 at Microsoft

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

01 Apr, 26

Salary

0.0

Posted On

01 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Artificial Intelligence, Statistical Analysis, Data Preparation, Modeling, SQL, Python, R, Data Visualization, Software Engineering, Debugging, Big Data, Cloud Computing, Responsible AI, Collaboration

Industry

Software Development

Description

Overview 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. Responsibilities 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. Business Understanding and Impact Leverages understanding of data science and business to examine a project and consider factors that can influence final outcomes within a technical area. Evaluates project plan for resources, risks, contingencies, requirements, assumptions, and constraints. Documents key business objectives. Effectively communicates business goals in analytical and technical terms. Consistently shares insights with stakeholders. Data Preparation and Understanding Acquires necessary data for project completion and describes it using querying, visualization, and reporting techniques. Explores data for key attributes and contributes to development of quality reports. Collaborates with others to perform data-science experiments using established methodologies and tools. Partners with Solution Architects, Consultants, and Data Engineers in data preparation efforts. Identifies data integrity problems and adheres to Microsoft's privacy policy. Modeling and Statistical Analysis Applies machine learning knowledge to identify the best approach for project objectives, utilizing individual algorithms and modeling techniques. Selects the appropriate approach to prepare data, train, optimize, and evaluate the model for statistical and business significance. Writes scripts in SQL, Python, R, etc. Designs experiments, analyzes results, and communicates findings to stakeholders. Understands operational considerations for model deployment and partners with data engineering teams to develop operational models. Evaluation Understands the relationship between the model and business objectives. Tests models on test and production data, analyzes performance, and incorporates customer feedback. Reviews data analysis and modeling techniques to identify overlooked or reexamined factors. Contributes to the review summary. 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 efficient and readable code for specific features, collaborating with other engineering teams to optimize code and improve system efficiency, reliability, and maintainability. Develops expertise in debugging techniques and integrates data models into customer systems. Understands big-data software engineering concepts, such as Hadoop Ecosystem, Apache Spark, CI/CD, Docker, Delta Lake, MLflow, AML, and REST API consumption/development. Business Management Develops understanding of data structures and relationship to customer business goals, observes senior engineers for best practices in identifying growth opportunities and exploring ML applications. Understands customer business goals and demonstrates a strong commitment to Responsible AI, supporting customers, partners and internal stakeholders in building trustworthy AI solutions. Customer/Partner Orientation Focuses on customer needs, manages expectations, and enhances customer excellence. Learns from senior team members to develop insights and communicate results. Understands the impact of data quality on model accuracy and can explain it to customers. Other Embody our culture and values Qualifications Required/Minimum Qualifications Bachelor's degree in Data Science, Computer Science, Engineering, Statistics, Operations Research, or a related field, with proven 4 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. Hands-on software engineering experience (e.g. Python, Scikit, PyTorch,C++) with main established data science frameworks. Additional or Preferred Qualifications 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. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. 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 with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Responsibilities

The Data Scientist will leverage data science and business domain knowledge to improve business performance and communicate insights with stakeholders. They will also apply machine learning and statistical analysis to develop and evaluate models, ensuring data integrity and collaborating with engineering teams.