Morgan Stanley

Data Scientist - Vice President - Data & Analytics Engineering

Posted On

Aug. 8, 2025

Location

Bengaluru, Karnataka

Experience Level

Freshers/Experienced

Job Type

Permanent

Apply By

Sept. 3, 2025

Job Description

Morgan Stanley

Data Scientist - Vice President - Data & Analytics Engineering

Profile Description

We’re seeking someone to join our team as (Vice President) as a Data Scientist. The Data Scientist is a crucial pillar in our organization, translating complex problems into actionable insights through the power of data. You will collaborate with cross-functional teams to analyze large datasets, build predictive models, and develop data-driven solutions that enhance strategic decision-making and drive organizational growth. Our ideal candidate is curious, creative, and passionate about leveraging statistical and machine learning techniques to solve real-world challenges. This role requires a self-starter with strong execution skills and the ability to work independently. You will be expected to not only execute on the current strategy but also contribute to its evolution. We are also looking for candidates who aspire to contribute to the broader data architecture community and become thought leaders in the space. We value diversity of thought and are committed to building a team that reflects the diversity of our global community.

Enterprise_Technology

Enterprise Technology & Services (ETS) delivers shared technology services for Morgan Stanley supporting all business applications and end users. ETS provides capabilities for all stages of Morgan Stanley’s software development lifecycle, enabling productive coding, functional and integration testing, application releases, and ongoing monitoring and support for over 3,000 production applications. ETS also delivers all workplace technologies (desktop, mobile, voice, video, productivity, intranet/internet) in integrated configurations that boost the personal productivity of employees. Application and end user functions are delivered on a scalable, secure, and reliable infrastructure composed of seamlessly integrated datacenter, network, compute, cloud, storage, and database functions.

Architecture & Modernization

Architecture & Modernization Drives development of the global firm strategy to define modern architectures and guardrails to reduce legacy debt, while partnering with app dev to accelerate the adoption of modern capabilities

Data & Analytics Engineering

This is Vice President position that provides specialist data analysis and expertise that drive decision-making and business insights as well as crafting data pipelines, implementing data models, optimizing data processes for improved data accuracy and accessibility, including applying machine learning and AI-based techniques.

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 for those who show passion and grit in their work.

Interested in joining a team that’s eager to create, innovate and make an impact on the world? Read on…

What you’ll do in the role:

Collect, process, and analyze large volumes of structured and unstructured data from diverse sources. · Develop and implement machine learning algorithms and statistical models specifically aimed at analyzing technology metrics, such as system performance, resource utilization, adherence to technology principles etc there by helping achieve improved resource utilization, boosting system reliability, reducing technical debt etc .
Generate actionable insights to guide strategic decisions and drive improvements in technology operations.
Work closely with business stakeholders to understand their objectives, formulate analytical questions, and translate requirements into data-driven solutions. · Perform exploratory data analysis (EDA) to uncover patterns, trends, and actionable insights.
Visualize and communicate findings using compelling reports, dashboards, presentations, and data storytelling techniques tailored to technical and non-technical audiences.
Collaborate with engineering teams to deploy predictive models into production environments and ensure their reliability, scalability, and performance.
Monitor, evaluate, and enhance model performance over time, implementing improvements as necessary.
Stay up to date with the latest research, tools, and best practices in data science, machine learning, and artificial intelligence.
Contribute to a culture of experimentation, continuous learning, and innovation within the organization.

What you’ll bring to the role:

10+ years of being a practitioner in data engineering or a related field.
At least 6 years' relevant experience would generally be expected to find the skills required for this role
Bachelor’s or master’s degree in computer science, Statistics, Mathematics, Data Science, Engineering, or a related quantitative field.
Strong programming skills in languages such as Python or R; familiarity with SQL and distributed computing frameworks (e.g., Spark, Hadoop) is a plus.
Hands-on experience with machine learning libraries and frameworks, such as scikit-learn, TensorFlow, Keras, PyTorch, or XGBoost.
Proficiency in data visualization tools such as Matplotlib, PowerBI, Tableau.
Solid understanding of probability, statistics, hypothesis testing, and data modeling concepts.
Experience with cloud platforms (e.g., AWS, GCP, Azure) for data storage, processing, and model deployment is beneficial.
Excellent communication and collaboration skills, with the ability to explain complex technical concepts to diverse audiences.
Strong attention to detail, analytical thinking, and problem-solving abilities.

Preferred Qualifications

Experience working on large-scale data science projects or in industry domains such as finance, healthcare, retail, or technology.
Familiarity with MLOps practices, model versioning, and monitoring tools.
Knowledge of natural language processing (NLP), computer vision, or time-series analysis.
Contributions to open-source projects or publications in relevant conferences/journals.
Develop and maintain data pipelines and ETL processes to ensure reliable and scalable data flow where applicable.

Key Competencies

Analytical Expertise: Comfortable working with large datasets, extracting meaningful insights, and interpreting ambiguous data patterns.
Technical Proficiency: Deep understanding of machine learning, statistical modeling, and data engineering principles.
Communication: Excellent written and verbal communication skills, with the ability to present insights effectively to senior leadership and non-technical stakeholders.
Collaboration: Team-oriented mindset, capable of building strong partnerships with colleagues across various departments.
Innovation: Commitment to creative problem-solving and willingness to experiment with novel approaches and technologies.

WHAT YOU CAN EXPECT FROM MORGAN STANLEY:

We are committed to maintaining the first-class service and high standard of excellence that have defined Morgan Stanley for over 89 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 opportunities 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.

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Job Description

Morgan Stanley

Data Scientist - Vice President - Data & Analytics Engineering

Profile Description

Enterprise_Technology

Architecture & Modernization

Architecture & Modernization Drives development of the global firm strategy to define modern architectures and guardrails to reduce legacy debt, while partnering with app dev to accelerate the adoption of modern capabilities

Data & Analytics Engineering

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.

Interested in joining a team that’s eager to create, innovate and make an impact on the world? Read on…

What you’ll do in the role:

Generate actionable insights to guide strategic decisions and drive improvements in technology operations.

Work closely with business stakeholders to understand their objectives, formulate analytical questions, and translate requirements into data-driven solutions. · Perform exploratory data analysis (EDA) to uncover patterns, trends, and actionable insights.

Visualize and communicate findings using compelling reports, dashboards, presentations, and data storytelling techniques tailored to technical and non-technical audiences.

Collaborate with engineering teams to deploy predictive models into production environments and ensure their reliability, scalability, and performance.

Monitor, evaluate, and enhance model performance over time, implementing improvements as necessary.

Stay up to date with the latest research, tools, and best practices in data science, machine learning, and artificial intelligence.

Contribute to a culture of experimentation, continuous learning, and innovation within the organization.

What you’ll bring to the role:

10+ years of being a practitioner in data engineering or a related field.

At least 6 years' relevant experience would generally be expected to find the skills required for this role

Bachelor’s or master’s degree in computer science, Statistics, Mathematics, Data Science, Engineering, or a related quantitative field.

Strong programming skills in languages such as Python or R; familiarity with SQL and distributed computing frameworks (e.g., Spark, Hadoop) is a plus.

Hands-on experience with machine learning libraries and frameworks, such as scikit-learn, TensorFlow, Keras, PyTorch, or XGBoost.

Proficiency in data visualization tools such as Matplotlib, PowerBI, Tableau.

Solid understanding of probability, statistics, hypothesis testing, and data modeling concepts.

Experience with cloud platforms (e.g., AWS, GCP, Azure) for data storage, processing, and model deployment is beneficial.

Excellent communication and collaboration skills, with the ability to explain complex technical concepts to diverse audiences.

Strong attention to detail, analytical thinking, and problem-solving abilities.

Preferred Qualifications

Experience working on large-scale data science projects or in industry domains such as finance, healthcare, retail, or technology.

Familiarity with MLOps practices, model versioning, and monitoring tools.

Knowledge of natural language processing (NLP), computer vision, or time-series analysis.

Contributions to open-source projects or publications in relevant conferences/journals.

Develop and maintain data pipelines and ETL processes to ensure reliable and scalable data flow where applicable.

Key Competencies

Analytical Expertise: Comfortable working with large datasets, extracting meaningful insights, and interpreting ambiguous data patterns.

Technical Proficiency: Deep understanding of machine learning, statistical modeling, and data engineering principles.

Communication: Excellent written and verbal communication skills, with the ability to present insights effectively to senior leadership and non-technical stakeholders.

Collaboration: Team-oriented mindset, capable of building strong partnerships with colleagues across various departments.

Innovation: Commitment to creative problem-solving and willingness to experiment with novel approaches and technologies.

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