Data Scientist

Key Skills

AI technology, C++, Continuous Integration & Continuous Delivery - CI/CD, Data science techniques, Python Programming, R Programming, SQL

Job Description

Overview

Are you a data scientist, and you 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, and engineering standards to set our sales and delivery teams up for success. Leveraging the principles of model, care, and coach, we provide consistent high-quality customer experience through technical leadership and IP capture centred 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 Data Scientist with experience in and passion for advanced statistical data analysis.

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

• Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, 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, 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, or related field
  • OR equivalent experience.

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
• Regular interaction with internal and external stakeholders on large, complex projects
• Bachelor's or Master’s degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or equivalent data-science experience
• Proven ability so speak Spanish fluently.

Responsibilities

​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