Data Science Analyst - FinLab

Key Skills

google-adwords, Java Programming, Python Programming, SQL, Azure, AWS, Scala Programming, Data science techniques, Google Cloud Platform (GCP), Amazon EMR, SPARK Programming, Databricks, ETL frameworks

Job Description

You will be based in our Waltham, Chicago or Atlanta office with our FinLab team.

You will work with analytics colleagues, software engineers and architects, McKinsey consultants and clients to carry out complex analyses, build statistical and machine learning models, and develop actionable recommendations for a wide-range of strategic topics in industries such as financial services, real estate, retail and public sector.

McKinsey works with clients in many industries to build custom software solutions that combine deep industry expertise, strategic insights and AI/ML analytics to solve high impact business problems.

What You'll Do

You'll work with client teams to apply machine learning models and advanced analytics to a large variety of business problems.

In this role, you will work with a team of McKinsey and client software engineers to deliver the analytics-driven insights through a user friendly interface such as a web-based application or a business intelligence tool (e.g. Power BI). You will work with the client IT team as they set up the necessary infrastructure and processes to support predictive analytics (e.g. ETL from the ERP system to data warehouse/lake). You'll work with McKinsey client teams on analytics methodologies and approaches to address client-specific needs including discussing data collection, architecture, associated costs and trade-offs, and recommendations.

You'll also own an analytics workstream, manage respective tasks and client project team members/experts while building client relationships. Working on projects and exchanging experiences with your colleagues means you will face new intellectual challenges on a daily basis while continuously building your methodological knowledge and skills. You will also keep abreast on the latest technologies and analytics approaches, attending conferences or other events as needed.

Qualifications

• Advanced degree in a STEM-related field (e.g. ops research, statistics, applied math, engineering, business analytics)
• 2 years of related professional experience
• Deep understanding of statistical and predictive modeling concepts, machine-learning approaches, clustering and classification techniques, and recommendation and optimization algorithms
• Demonstrated proficiency in Python and SQL; additional knowledge of other data mining software (Spark, Scala, etc.) is a plus
• Knowledge of the major cloud platforms (AWS, Azure) and associated analytical and data tools (Databricks, EMR, RDS, S3)
• Ability to easily understand complex algorithm and logic to process data
• Experience working with a large volume of data with ability to solve performance issues
• Basic business intuition and clear expertise in analyses; experience describing analytic processes including when and why specific approaches are favored
• Excellent communication and presentation skills with an ability to visualize and report insights creatively in variety of formats to various stakeholders