Data Scientist [Multiple Positions Available]

JPMorganChase•Jersey City, WA

14h•Onsite

About The Position

This role involves utilizing data analytics to generate actionable insights that support business objectives. The Data Scientist will apply advanced data analysis techniques to identify trends, patterns, and opportunities within large data sets. They will develop and implement data models to predict future outcomes and inform strategic decisions, employing machine learning techniques to enhance data analysis and predictive capabilities. This includes designing, training, and validating machine learning models to improve the accuracy and efficiency of data-driven insights, and integrating these algorithms into existing data processes to automate and optimize analytical tasks. The position also requires providing strategic guidance to business partners based on data-driven insights, presenting clear and concise recommendations, and collaborating with partners to understand their needs and tailor insights to support their specific goals. Ultimately, the role supports the achievement of the Firm's strategic goals and objectives through data-driven decision-making, monitoring, and evaluating the impact of recommendations.

Requirements

Master's degree in Analytics, Statistics, Mathematics, Data Science or related field of study plus three (3) years of experience in the job offered or as Data Scientist, Data Science Apprentice, or related occupation.
Three (3) years of experience with leading full-cycle analytics projects using CRISP DM methodology, including gathering requirements from business stakeholders, preparing ETL workflow and cleaning data using SQL, Python, Hadoop, Teradata SQL, and Snowflake Data Cloud, building analysis and validating results, and assessing business impact.
Three (3) years of experience with manipulating datasets using software and platforms including Python, SQL, Hadoop, PySpark, Alteryx, Snowflake, and Teradata SQL to create bespoke analytical record.
Three (3) years of experience with developing automated scripts using Python & PySpark for data quality checks, and orchestration via Alteryx.
Three (3) years of experience with implementing and enforcing data regulation controls, ensuring proper use and thorough documentation of the analytical process with Jira.
Three (3) years of experience with building and deploying supervised machine learning models including classification models using Python, including Random Forests, Gradient Boosting Machines, and Decision Trees techniques to create actionable customer personas and compile feature importance analysis.
Three (3) years of experience with applying unsupervised learning methods, including K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), & Gaussian mixture Models (GMM), to create unbiased data lead customer profile segments and recommend optimal price bands.
Three (3) years of experience with building price elasticity models using Regression methods and Support Vector Machines in combination with sensitivity analysis framework to understand various cost benefits of pricing strategies & identifying optimal pricing for products.
Three (3) years of experience with applying causal inference techniques including Difference-in-Differences and Causal Forests, and utilizing quasi-experimental methods to estimate heterogeneous treatment effects.
Three (3) years of experience with conducting matched pairs analysis to evaluate behavioral and profitability differences between customer segments using Propensity Score Matching to obtain Propensity Score weighting.
Three (3) years of experience with designing and executing end-to-end analytical measurement consistent with learning agenda for initiatives, including requirement gathering, A/B testing including statistical tests, Pre/Post measure, designing experiment and other Test-Control strategies to measure uplift and optimize initiatives.
Three (3) years of experience with creating comprehensive dashboards and visualizations in Tableau, supporting effective storytelling and business decision-making.
One (1) year of experience with applying consumer banking and finance concepts including deposit products, checks, savings, CDs, fee revenue, and deposit margin to drive data analysis and inform business strategy development.

Responsibilities

Utilize data analytics to generate actionable insights that support business objectives.
Apply advanced data analysis techniques to identify trends, patterns, and opportunities within large data sets.
Develop and implement data models to predict future outcomes and inform strategic decisions.
Employ machine learning techniques to enhance data analysis and predictive capabilities.
Design, train, and validate machine learning models to improve the accuracy and efficiency of data-driven insights.
Integrate machine learning algorithms into existing data processes to automate and optimize analytical tasks.
Provide strategic guidance to business partners based on data-driven insights.
Present clear and concise recommendations derived from data analysis to inform business strategies and initiatives.
Collaborate with business partners to understand their needs and tailor data insights to support their specific goals.
Support the achievement of the Firm's strategic goals and objectives through data-driven decision-making.
Align data analytics efforts with the Firm's overall strategic plan to ensure that insights contribute to key business priorities.
Monitor and evaluate the impact of data-driven recommendations on business performance and adjust strategies as needed.