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Morgan Stanley

Machine Learning & Workplace Analytics, WM Administration, Vice President, Wealth Management

Morgan Stanley is hiring a Machine Learning & Workplace Analytics, WM Administration, Vice President, Wealth Management in Mumbai, India. Level rates it ; you can apply on Morgan Stanley.

Level's read on this job

  • On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
  • The posting mentions PyTorch, TensorFlow and LLMs among its tools and skills.
  • Level classes it as a senior role.
  • Morgan Stanley has 1,200+ other open roles on Level.

AI in this role

Build end-to-end machine learning models and generative AI use cases for wealth management as Vice President.

PyTorchTensorFlowLLMs
ml-opsmachine-learninggenerative-aipredictive-modelinganalyticswealth-management

Company Profile:
Morgan Stanley is an industry leader in financial services, known for mobilizing capital to help governments, corporations, institutions, and individuals around the world achieve their financial goals.

At Morgan Stanley India, we support the Firm's global businesses, with critical presence across Institutional Securities, Wealth Management and Investment management, as well as in the Firm's infrastructure functions of Technology, Operations, Finance, Risk Management, Legal and Corporate & Enterprise Services. Morgan Stanley has been rooted in India since 1993, with campuses in both Mumbai and Bengaluru. We empower our multi-faceted and talented teams to advance their careers and make a global impact on the business. For those who show passion and grit in their work, there's ample opportunity to move across the businesses.

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 can work as a player-coach. The role involves focusing on developing Machine Learning models and Gen AI use cases across WM Business lines. The candidate will be responsible for building end to end Machine learning solutions, coordinating with technology and risk partners to operationalize the model, monitoring and recalibrating the built models, and communicating the impact of models to key stakeholders. The role also requires leveraging LLMs to build Gen AI use cases supporting all WM business verticals.

This role will report to the Head of Analytics & Data Mumbai, joining a cohort of other ambitious and enthusiastic associates, driven by ideas and with a strong proclivity towards strategic analytics. We are looking for someone willing to think beyond traditional organizational structures and with a strong desire to enhance the data and deepen the analytics.


Key Responsibilities:
Key responsibilities will include but will not be limited to the following:

  • Design and develop end-to-end machine learning solutions that address key WM business opportunities and produce measurable outcomes.
  • Strive to develop and experiment with State-of-the-Art algorithms.
  • Partner with model risk stakeholders to validate models and meet reliability and governance expectations.
  • Deploy the machine learning models in production environments, in collaboration with the MLOps team, and monitor their performance.
  • Plan and conduct A/B tests to quantify incremental impact and effectiveness of ML solutions.
  • Build, grow, and establish partnerships with business stakeholders as well as with our Risk, Legal, and Compliance divisions.
  • Create presentations to effectively communicate model performance, insights, and recommendations to stakeholders and the broader team.
  • Manage and coach associates. Experience:
  • Master's or a PhD degree (preferred) in Computer Science, Engineering, Mathematics, Physics, or an equivalent quantitative field.
  • A minimum of 5 years of professional experience in Machine Learning (overall 10-14 years), preferably in the financial services industry


Required Skills:

  • Demonstrated breadth and depth in knowledge and applications of machine learning algorithms in classification, regression, recommender systems, clustering, deep learning
  • Proficiency in autonomously conducting applied ML research with commercial applications.
  • 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, PyTorch - Geometric, or equivalent).
  • Experience with Graphical Neural Networks, Reinforcement Learning, LLMs, Transformer based Models, or Recommender Systems is a plus.
  • Track record of publishing in peer-reviewed scientific journals.

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.

How we rate this

Machine Learning & Workplace Analytics, WM Administration, Vice President, Wealth Management at Morgan Stanley rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsMachine learningGenerative AIPredictive ModelingAnalyticsWealth ManagementPyTorchTensorFlow

Questions you could be asked

  1. How do you monitor a model once it's live, and how do you know it needs retraining?
  2. Tell me about a project where machine learning was part of your work. What did you do?
  3. Tell me about a project where generative ai was part of your work. What did you do?
  4. Tell me about a project where predictive modeling was part of your work. What did you do?
  5. Tell me about a project where analytics was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: ML Ops, Machine learning, Generative AI, Predictive Modeling, and Analytics. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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