Machine Learning & Workplace analytics, WM Administration, Associate, Wealth Management
Morgan Stanley · Mumbai, Maharashtra, India
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- On site
- Full-time
- $120,000 / year
- Mumbai, Maharashtra, India
Job highlights
- Build ML models for Wealth Management business.
- Generate predictive analytics and business intelligence.
- Monitor and recalibrate models in production.
- Collaborate with business and control partners.
- Present model results to stakeholders.
About the role
Machine Learning Engineer Wealth Management
Morgan Stanley is an equal opportunity employer. We work to provide a supportive and inclusive environment where all individuals can maximize their full potential. Our skilled and creative workforce is comprised of individuals drawn from a broad cross section of the global communities in which we operate and who reflect a variety of backgrounds, talents, perspectives, and experiences. Our strong commitment to a culture of inclusion is evident through our constant focus on recruiting, developing, and advancing individuals based on their skills and talents.
Department Profile
The Analytics & Data (A&D) organization is a key growth area within Morgan Stanley's Wealth Management Division, playing a critical role in the execution of the wider Wealth Management strategy. The team owns all the executive reporting, insights, and predictive modeling in support of Wealth Management business. The use of analytics and data will be a key driver in accelerating growth across client segments, enabling data-driven decision making and delivering the best client experience.
Position Summary
We are looking for an experienced professional to join the Analytics & Data organization who will be responsible for building ML models for WM business verticals. The candidate will work with data in a hands-on capacity to build models and generate predictive analytics, insights, and business intelligence that will help decision making and strategy. They will be responsible for monitoring and recalibration of models and delivery of their outcomes to US-based team members and leadership. The ideal candidate will display ability to work with minimal direction, curiosity about data, and the ability to determine and build timely solutions.
Key Responsibilities
- Support the development of machine learning solutions for Wealth Management use cases, with guidance from senior team members.
- Experiment with different modeling approaches (including newer techniques) to improve performance and learn best practices.
- Work with model risk and validation partners to help document, test, and confirm model behavior and controls.
- Support deployment and monitoring of models in production in partnership with MLOps and engineering, including basic troubleshooting and performance checks.
- Assist with A/B tests or controlled experiments to measure impact and summarize results clearly.
- Collaborate with business and control partners (Risk, Legal, Compliance) to ensure solutions are usable and follow required standards.
- Create clear slides or performance readouts that communicate model results to business stakeholders.
Experience
- Bachelor's or Master's degree (preferred) in Computer Science, Engineering, Mathematics, Physics, or an equivalent quantitative field.
- Associate: A minimum of 3 years of experience in Machine Learning domain (overall 3-5.5 years), preferably in the financial services industry.
Required Skills
- Possess theoretical knowledge and applications of machine learning algorithms in classification, regression, recommender systems, clustering, deep learning.
- Proficiency in at least one of the modern programming languages (Python, C++, or a related language).
- Experience with code versioning systems such as Github, Bitbucket, and experiment tracking systems like ML Flow.
- Proficiency with computer science fundamentals in object-oriented design, data structures, and algorithmic design.
- Track record of working independently and solving problems creatively, as well as the ability to debug/maintain complex codes, with a strong sense of accountability and an eye for innovation.
- Excellent oral and written communication skills, including the ability to present complex information in a clear and concise manner to audiences of various backgrounds/seniority; 2+ years in a client-facing position preferred.
- Ability to work in a collaborative, transparent style within the team and with cross-functional stakeholders across the organization.
Preferred Skills
- Experience with Cloud or Big Data technologies such as Azure, AWS, Google Cloud, Hadoop, or an equivalent.
- Familiarity with Deep Learning frameworks (PyTorch, Tensorflow, Py - Geometric, or equivalent).
Registration Required
- None
What You Can Expect From Morgan Stanley
At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.
Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.
Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.
For more information, please visit: https://www.morganstanley.com/people-opportunities/eeo.
Key skills/competency
- Machine Learning
- Python
- Data Science
- Predictive Modeling
- Wealth Management
- Cloud Technologies
- Deep Learning
- Algorithm Design
- Communication Skills
- Problem Solving
Skills & topics
- Machine Learning
- Wealth Management
- Python
- Data Science
- Predictive Modeling
- Analytics
- Financial Services
- Cloud Computing
- Big Data
- Deep Learning
- Associate
- Morgan Stanley
How to get hired
- Tailor your resume: Highlight your machine learning, Python, and financial services experience. Quantify achievements.
- Showcase your skills: Emphasize proficiency in ML algorithms, programming languages, and version control systems like Github.
- Demonstrate problem-solving: Provide examples of independent work, creative solutions, and debugging complex code.
- Prepare for interviews: Be ready to discuss your experience with model risk, deployment, and presenting technical information to non-technical audiences.
Technical preparation
Behavioral questions
Frequently asked questions
- What is the primary focus of the Machine Learning Engineer role at Morgan Stanley Wealth Management?
- The primary focus of this Machine Learning Engineer role at Morgan Stanley Wealth Management is to build and deploy ML models for business verticals, generate predictive analytics, and provide insights to aid strategic decision-making. You will also be responsible for monitoring and recalibrating these models.
- What level of experience is required for the Associate Machine Learning Engineer position at Morgan Stanley?
- For the Associate level, Morgan Stanley requires a minimum of 3 years of experience in the Machine Learning domain, with an overall experience range of 3-5.5 years. Experience in the financial services industry is preferred.
- What programming languages and tools are essential for this Machine Learning Engineer role?
- Proficiency in at least one modern programming language like Python or C++ is essential. Experience with code versioning systems such as Github or Bitbucket, and experiment tracking systems like ML Flow, is also required.
- Does Morgan Stanley prefer candidates with a specific degree for their Machine Learning Engineer positions?
- Morgan Stanley prefers candidates with a Bachelor's or Master's degree in a quantitative field such as Computer Science, Engineering, Mathematics, or Physics. Equivalent quantitative experience is also considered.
- What are the key responsibilities for a Machine Learning Engineer at Morgan Stanley's Analytics & Data team?
- Key responsibilities include developing ML solutions, experimenting with modeling approaches, documenting and testing models with risk partners, supporting model deployment and monitoring, assisting with A/B tests, collaborating with business partners, and creating performance readouts for stakeholders.
- What are Morgan Stanley's preferred skills for a Machine Learning Engineer role?
- Preferred skills include experience with Cloud or Big Data technologies (Azure, AWS, Google Cloud, Hadoop) and familiarity with Deep Learning frameworks like PyTorch or Tensorflow.
- How does Morgan Stanley foster a supportive work environment for its employees?
- Morgan Stanley fosters a supportive and inclusive environment by focusing on recruiting, developing, and advancing individuals based on their skills and talents. They value diversity and offer comprehensive employee benefits and perks.