Regulatory Capital Data Analyst and Automation Specialist
Warszawa, Mazowieckie, Poland
JPMorgan Chase & Co.
You are a strategic thinker with a strong technical and analytical mindset, passionate about developing impactful solutions, optimizing processes, and analyzing large datasets, that role is for you!
As a Regulatory Capital Data Analyst and Automation Specialist within the Basel Measurement and Analytics (BM&A) group within Capital Management you will be dedicated to producing and analyzing firm-wide Counterparty Credit risk regulatory capital results on a monthly basis. This team plays a crucial role in implementing Basel rules and generating risk-weighted assets numbers for the firm, ensuring compliance and strategic capital management.
You will be at the forefront of data analysis, design, and development of automated solutions, enabling us to deliver strategic systemic sourcing and implement enterprise solutions. Your role involves close collaboration with team members globally to ensure a unified approach to capital management. Additionally, you will work closely with Lines of Business, Model Implementation, Technology, and Regulatory Policy teams to advance the firm's capital agenda in Retail Credit risk, driving innovation and efficiency in capital management processes.
Job responsibilities:
- Implement new business initiatives by identifying data elements within the firm, updating models, collaborating with stakeholders, and partnering with Technology.
- Gather and document requirements by analyzing business needs, and design and develop technical solutions to address these needs.
- Provide technical expertise and recommendations for selecting and developing intelligent automation tools like Alteryx, Databricks, and Python.
- Perform data research and analysis using SQL queries, and create dashboards using Databricks and Tableau.
- Understand upcoming rules and regulations in the Retail domain, determine data gaps, draft resolution plans, and create tactical implementation solutions with a path for strategic enterprise implementation.
- Partner with risk management, lines of business, and technology teams to identify and remediate data quality issues.
Required qualifications, capabilities, and skills:
- 3+ years of experience in Python development, with a proven track record of designing, developing, and deploying Python solutions in Banking/Finance.
- Hands-on experience with Python and its libraries/frameworks (NumPy, PySpark, Pandas, Matplotlib, Seaborn) for automating data transformations and calculations
- Exposure to automation and developing machine learning algorithms to detect anomalies
- Bachelor’s degree in Finance/Quantitative Finance or CFA charter holders with a strong technical skillset, or Computer Science graduates with a finance industry background.
- Strong analytical and problem-solving skills with a focus on controls, and superior attention to detail and process orientation.
- Extensive experience with SQL for data analytics.
- Good interpersonal, oral, and written communication skills for effective collaboration.
- Ability to multi-task, prioritize, make decisions, and be organized, self-motivated, and a team player.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Banking Computer Science Credit risk Data analysis Data Analytics Databricks Data quality Finance Machine Learning Matplotlib NumPy Pandas PySpark Python Research Seaborn SQL Tableau
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