Applied AI/ML Associate Senior - Causal ML at JPMC Candidate Experience page

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

13 Sep, 26

Salary

0.0

Posted On

15 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Causal Inference, Machine Learning, Statistical Modeling, Python, Natural Language Processing, Tensorflow, Keras, Pytorch, Scikit-learn, Spark, Hive, SQL, Applied Experimentation, Causal ML, Data Science, Quantitative Analysis

Industry

Financial Services

Description

Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team. Our team focuses on applying GenAI, ML and statistical models to solve business problems in the Global Wealth Management space. As an Applied AI/ML Senior Associate within our dynamic team in Asset and wealth Management , you will apply your quantitative, data science, and analytical skills to complex problems. We are seeking a Data Scientist with strong foundations in causal inference, machine learning, statistical modeling, and applied experimentation to help build next-generation decision systems across pricing, campaign targeting, and related business use cases. This role is ideal for someone who can move beyond prediction and help the organization understand cause-and-effect relationships in real-world, observational settings. Job responsibilities • Engage with stakeholders and understanding business requirements • Develop AI/ML solutions to address impactful business needs • Work with other team members to productionize end-to-end AI/ML solutions • Engage in research and development of innovative relevant solutions • Document developed AI/ML models to stakeholders • Coach other AI/ML team members towards both personal and professional success • Collaborate with other teams across the firm to attain the mission and vision of the team and the firm   Required qualifications, capabilities, and skills * Strong quantitative training in Statistics, Data Science, Economics, Computer Science, Applied Mathematics, Operations Research, or a related field. * Strong understanding of causal inference fundamentals, including confounding, mediation, selection bias, and identification assumptions. * Practical knowledge of techniques used to control for confounding and estimate causal effects in observational data. * Familiarity with causal reasoning concepts such as backdoor criterion, frontdoor criterion, and treatment effect estimation. * Advanced degree in analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis, Operations Research) * Experience in the application of AI/ML to a relevant field * Demonstrated practical experience in machine learning techniques, supervised, unsupervised, and semi-supervised * Strong experience in natural language processing (NLP) and its applications * Solid coding level in Python programming language, with experience in leveraging available libraries, like Tensorflow, Keras, Pytorch, Scikit-learn, or others, to dedicated projects * Previous experience in working on Spark, Hive, and SQL   Preferred qualifications, capabilities, and skills * Industry experience applying causal machine learning to pricing, marketing, campaign targeting, personalization, or customer analytics. * Experience with temporal causality, longitudinal data, panel data, or dynamic treatment effects. * Experience with time series forecasting or combining causal inference with time-dependent modeling. * Familiarity with experimentation, A/B testing, quasi-experimental design, or synthetic control methods. * Experience with modern causal ML methods such as meta-learners, uplift models, causal forests, or double machine learning. * Financial service background .PhD/Masters   JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management. 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 [https://careers.jpmorgan.com/us/en/how-we-hire/faqs] for more information about requesting an accommodation. J.P. Morgan Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.

Responsibilities

Develop and productionize AI/ML solutions focusing on causal inference to solve business problems in Global Wealth Management. Engage with stakeholders to understand requirements and coach team members toward professional success.