Data Scientist and MI Manager

at  MetLife

Working Schedule
Full-Time
Work Arrangement
Hybrid
Relocation Assistance Available
No
Posted Date
01-Nov-2024
Job ID
4930

WHAT YOU NEED TO SUCCEED (REQUIRED QUALIFICATIONS)

• MSc in Computer Science, Statistics, Mathematics, Information Systems or related Technical Discipline
• Experience in Data Science role
• Deep knowledge of SQL, “Big data” data pipelines, architectures, machine learning and data sets
• Experience working with big data tools.
• Experience working with large data sets, data pipeline and workflow management tools, Azure cloud services, and stream-processing system.
• Background in programming in Python or R
• Experience working in AI startup environment or organisations with an agile culture
• Professional attitude and service orientation; superb team player
• Good written and verbal communication skills along with strong desire to work in cross-functional teams
• Able to build a sense of trust and rapport that creates a comfortable & effective workplace; collaborative
• Attitude to thrive in a fun, fast-paced, startup-like environment
• Open-minded to new approaches and learning

WHAT CAN GIVE YOU AN EDGE (ADDITIONAL SKILLS)

• Java, C, C++, Spark, Pandas, Numpy, SciPy, Statsmodels, Stan, Pumc3, Caret, Scikit-learn, Keras, TensorFlow, Pytorch

• Understands business problems and designs end to end analytics use cases.
• Acts as a change agent with an ownership mindset over business problem.
• Develops complex models and algorithms that drive innovation throughout the organisation. This may include improving on-time performance, network planning, etc..
• Conducts advanced statistical and other analysis to provide actionable insights, identify trends, and measure performance.
• Provides coaching and insight on analytic approaches and can objectively weigh trade-offs of different analytic methods.
• Guide data engineering efforts to ensure alignment with future data science needs.
• Provides guidance to business leaders and data engineering function highlighting data integrity risks.
• Collaborates with analytics engineers to implement and deploy scalable solutions.
• Provides thought leadership by researching best practices, conducting experiments and collaborating with industry leaders.