Data Science Lead Analyst (Distribution Data Science Team)

Responsibilities

The Data Science Lead Analyst will play a key role on our data science team, leveraging their experience to deliver impactful data-driven solutions for complex business challenges
With a foundation in data analysis and model development, you will work alongside a team of data scientists and business strategists, contributing to the development of data-driven solutions for business challenges
Your work will be user case oriented: you will take on responsibility for the end-to-end process of extracting insights from large datasets
You are expected to work collaboratively with cross-functional teams, as well as independently manage projects, to drive strategic decision-making and innovation
You will constructively discuss your results and potential findings with members of the team and the other MFS Business Units
Design and implement advanced statistical models and machine learning algorithms to solve specific business problems
Interpret and analyze data from multiple sources to provide actionable insights and recommendations
Collaborate with business strategists to understand their needs and provide data-driven guidance
Take initiative in identifying opportunities for data analytics to add value to the business
Ensure data quality and integrity throughout all processes
Effectively communicate findings to stakeholders through visualization and presentations, translating complex data into clear business terms
Mentor Associate Data Scientists and contribute to their professional development
Stay up-to-date with the latest trends and technologies in data science and machine learning to bring fresh ideas to the team and to the organization

Skills

Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Operations Research, Engineering, Physics, or another quantitative discipline with 5-6 yrs experience, or Master's degree with 4 years of experience
Demonstrated proficiency in Python, SQL, and data visualization tools (such as Tableau, Power BI, or similar)
Strong experience in cleaning, transforming, and visualizing large data sets
Proven ability to develop, test, and deploy predictive models and machine learning algorithms
Advanced understanding of a wide array of machine learning and statistical techniques, with a track record of choosing the right model for the right problem
Familiarity with big data technologies (such as Hadoop, Spark, or similar) and cloud computing services (AWS, Azure, GCP) is desirable
Strong analytical and quantitative problem-solving ability
Experience in effectively communicating complex analytical findings and conclusions to non-technical stakeholders, including the creation of clear and actionable reports
Demonstrated ability to work independently on complex projects with minimal supervision as well as collaboratively with a team
A history of driving projects to completion and contributing to the success of those projects
Ability to mentor and support Associate Data Scientists in their technical development
Commitment to continuous learning and staying current with industry trends and best practices in data science

Benefits

💼 Generous time-off provided: including "Responsible time off" for many roles, paid company holidays when the US Stock Exchange is closed, plus paid volunteer time
👨‍👩‍👧‍👦 Family Focus: Up to 20 weeks of paid leave for new parents, back-up care program, dependent care flexible spending account, adoption assistance, generous caregiver leave
🏥 Health and Welfare: Competitive medical, vision and dental plans, plus tax-free health savings accounts with company contributions
🧘 Wellness Programs: Robust wellness webinars, employee assistance program, gym reimbursement through our medical plans, fitness center discounts and more
💼 Life & Disability Benefits: Company-paid basic life insurance and short-term disability
💰 Financial Benefits: 401(k) savings plan, Defined Contribution plan- 15% of base salary invested into the Plan, competitive total compensation programs MFS is a hybrid work environment (remote/onsite) unless otherwise stated in the job posting