Vice President, Data Management & Quantitative Analysis I
Pune, MH, India
BNY
BNY is a global financial services company overseeing nearly $50 trillion — managing it, moving it and keeping it safe.Vice President, Data Management & Quantitative Analysis I
At BNY, our culture empowers you to grow and succeed. As a leading global financial services company at the center of the world’s financial system we touch nearly 20% of the world’s investible assets. Every day around the globe, our 50,000+ employees bring the power of their perspective to the table to create solutions with our clients that benefit businesses, communities and people everywhere.
We continue to be a leader in the industry, awarded as a top home for innovators and for creating an inclusive workplace. Through our unique ideas and talents, together we help make money work for the world. This is what #LifeAtBNY is all about.
We’re seeking a future team member for the role of Vice President I to join our Data Management & Quantitative Analysis team. This role is located in Pune, MH or Chennai, TN (Hybrid).
In this role, you’ll make an impact in the following ways:
BNY Data Analytics Reporting and Transformation (“DART”) has grown rapidly and today it represents a highly motivated and engaged team of skilled professionals with expertise in financial industry practices, reporting, analytics, and regulation. The team works closely with various groups across BNY to support the firm’s Capital Adequacy, Counterparty Credit as well as Enterprise Risk modelling and data analytics; alongside support for the annual Comprehensive Capital Analysis and Review (CCAR) Stress Test.
The Counterparty Credit Risk Data Analytics Team within DART designs and develops data-driven solutions aimed at strengthening the control framework around our risk metrics and reporting. For the Counterparty Credit Risk Data Analytics Team, we are looking for a Counterparty Risk Analytics Developer to support our Counterparty Credit Risk control framework.
- Develop analytical tools using SQL & Python to drive business insights
- Utilize outlier detection methodologies to identify data anomalies in the financial risk space, ensuring proactive risk management
- Analyze business requirements and translate them into practical solutions, developing data-driven controls to mitigate potential risks
- Plan and execute projects from concept to final implementation, demonstrating strong project management skills
- Present solutions to senior stakeholders, effectively communicating technical concepts and results
- Collaborate with internal and external auditors and regulators to ensure compliance with prescribed standards, maintaining the highest level of integrity and transparency.
To be successful in this role, we’re seeking the following:
- A Bachelor's degree in Engineering, Computer Science, Data Science, or a related discipline (Master's degree preferred)
- At least 3 years of experience in a similar role or in Python development/data analytics
- Strong proficiency in Python (including data analytics, data visualization libraries) and SQL, basic knowledge of HTML and Flask.
- Ability to partner with technology and other stakeholders to ensure effective functional requirements, design, construction, and testing
- Knowledge of financial risk concepts and financial markets is strongly preferred
- Familiarity with outlier detection techniques (including Autoencoder method, random forest, etc.), clustering (k-means, etc.), and time series analysis (ARIMA, EWMA, GARCH, etc.) is a plus.
- Practical experience working with Python (Pandas, NumPy, Matplolib, Plotly, Dash, Scikit-learn, TensorFlow, Torch, Dask, Cuda)
- Intermediate SQL skills (including querying data, joins, table creation, and basic performance optimization techniques)
- Knowledge of financial risk concepts and financial markets
- Knowledge of outlier detection techniques, clustering, and time series analysis
- Strong project management skills
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Clustering Computer Science Credit risk CUDA Dart Data Analytics Data management Data visualization Engineering Flask NumPy Pandas Plotly Python Scikit-learn SQL TensorFlow Testing
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