VP-Machine Learning Engineer (12 - 15 Yrs)

Key Skills

Apache Hadoop, Apache Hive, Apache Impala, HBase, Java Programming, Apache Kafka, Python Programming, Scala Programming, SPARK Programming, SQL

Job Description

Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, wealth management and investment management services. With offices in more than 41 countries, the Firm's employees serve clients worldwide including corporations, governments, institutions and individuals. For further information about Morgan Stanley, please visit www.morganstanley.com.

Wealth Management Technology

Wealth Management Technology is responsible for the design, development, delivery, and support of the technical solutions behind the products and services used by the Morgan Stanley Wealth Management Business. Practice areas include: Analytics, Intelligence, & Data Technology (AIDT), Client Platforms, Core Technology Services (CTS), Financial Advisor Platforms, Global Banking Technology (GBT), Investment Solutions Technology (IST), Institutional Wealth and Corporate Solutions Technology (IWCST), Technology Delivery Management (TDM), User Experience (UX), and the CAO team.

Job Profile

• Create advanced analytics and machine learning driven solutions for varying data volumes, data types and formats. Design machine learning systems, and oversee the platform on which the solutions would be deployed
• Design and Build distributed, scalable, and reliable data pipelines that ingest and process data at scale and in real-time
• Work with business and cybersecurity team SME to understand requirements and develop relevant solutions. Create presentations / visualizations for the leadership and business that would be able to explain complex outcomes and emphasize business impact.
• Understand computer networks domain and conduct exhaustive literature survey to identify new threats, state-of-art techniques and ML applications
• Ensure data integrity through – Data quality, validation, Governance and Transparency
• Explore new data sources and data. Select appropriate datasets and their representation methods. Perform data analysis (statistical and otherwise), to derive inferences from data
• Production deployment and Model monitoring to ensure stable performance and adherence to standards
• Evaluate state-of-art data-centric technologies and prototype solutions to improve our architecture and platform
• Lead data science and engineering team in a distributed agile framework

QUALIFICATIONS

Skill Set 

• Experienced professional with 8-11 years of experience developing and implementing ML models in Big data ecosystem i.e. Hadoop, Spark, Kafka, HBase, Hive / Impala, Cassandra, MongoDB or any other similar distributed computing technology
• Expertise in applied Machine learning and statistical modeling techniques in Python / Java / Scala. Experience using ML platforms such as Dataiku / Databricks is a plus.
• Expertise in at least one Programming language – Python / Java / Scala
• Proficiency in data analysis using complex and optimized SQL and / or above mentioned technologies
• Expertise in visualizing large datasets and developing articulate dashboards in an efficient manner
• Proficiency in domains – Network analytics and Cybersecurity is a plus
• People and stakeholder management experience and excellent organizational skills and follow-through
• Ability to work in fast paced, dynamic and geographically distributed environment
• Good written and verbal communication skills

Good to Have skills

• In-depth understanding of Machine Learning and Statistics, in addition to above
• Practical knowledge of Natural Language Processing (NLP) and/or Neural network implementation
• Deep learning algorithms and applications