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Chase · 18 hours ago

Data Scientist [Multiple Positions Available]

3223 Hanover St, Palo Alto, CA, 94304, US

Full-time

Onsite

Entry Level

$149K/yr - $170K/yr

1+ years exp

Chase is one of the oldest financial institutions, offering innovative financial solutions to a wide range of clients. They are seeking a Data Scientist to build data solutions, extract insights, and collaborate with various business leaders to drive high-impact analytics and business decisions.

BankingFinancial Services

Responsibilities

Build data solutions and extract insights from big data environments to design metrics and conduct in-depth analyses that drive business decisions

Partner with leaders across the business of Sales, Marketing, Finance and Account Management to deliver high-impact analytics

Establish strong partnerships with our stakeholders across Chase Payment Solutions

Collaborate with analytics and non-analytics groups to design solutions and provide actionable insights

Drive the execution of analytics and data science initiatives

Analyze data from a variety of data sources and identify key insights

Demonstrate strong analytical ability through understanding and implementation of various analytical and statistical techniques to answer high-priority business questions

Interpret analytical results and present findings to stakeholders

Build data visualization and provide ongoing enhancements to business dashboards

Experiment, develop, and produce high-quality machine learning models that drive business growth

Qualification

TableauSQLPythonMachine LearningData VisualizationETL AutomationData AnalysisCommunication SkillsCollaborationProblem Solving

Required

Bachelor's degree in Data Science, Business Analytics, or related field of study plus 1 year of experience in the job offered or as Data Scientist, Business Analyst, or related occupation

Creating and maintaining dynamic and interactive dashboards using Tableau and Looker leveraging ETL automation, ODBC connector

Creating advanced visualizations including heat maps, drill-down charts and tools including parameters, and LOD (Level of Detail) expressions

Conducting in-depth exploratory data analysis within large enterprise databases (Terabytes- scale) using SQL via CTE and window functions

Transforming raw data into actionable insights using Python libraries including Pandas and NumPy for data manipulation, scikit-learn for machine learning, matplotlib, and seaborn for data visualization

Presenting data findings, model results, and performance metrics to cross-functional teams and translating complex analyses into strategic recommendations for business growth

Designing and building robust, scalable data processing and model training pipelines by leveraging Random Forest and K-Nearest Neighbors (KNN) machine learning algorithms

Developing, optimizing, and producing predictive modeling pipelines by Tree-based Pipeline Optimization Tool (TPOT) python libraries that automate hyperparameter tuning and model selection to enhance prediction performance and model efficiency

Automating and streamlining ETL workflows in order to produce recurring reports and data updates leveraging Excel and Alteryx workflows

Leverage advanced Excel functionalities including vlookup, index match, and pivot tables

PySpark and Snowpark data science libraries in Python

Data engineering pipelines for big data