Data Scientist II

Responsibilities

The role involves delivering machine learning and artificial intelligence concepts from start to finish, including data exploration, algorithm building, and model deployment. You will partner with product and engineering teams to solve complex business problems and communicate findings to non-technical stakeholders.

Requirements

Candidates must have a bachelor's degree in a quantitative field and at least 4 years of relevant work experience. Proficiency in Python or R, SQL, and experience with large-scale data sets and machine learning libraries are required.

Skills

• Python
• R
• SQL
• Machine learning
• Data science
• Feature engineering
• Data visualization
• PySpark
• Fraud analytics
• Risk modeling
• Statistical analysis
• Algorithm development
• Data pipelines
• Model validation
• Communication
• Problem solving

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• Health savings account
• Flexible spending account
• 401k retirement plan
• Paid time off
• Paid company holidays
• Paid volunteer day
• Paid parental leave
• Maven family planning

About this role

At Early Warning, we’ve powered and protected the U.S. financial system for over thirty years with cutting-edge solutions like Zelle®, Paze®, and so much more. As a trusted name in payments, we partner with thousands of institutions to increase access to financial services and protect transactions for hundreds of millions of consumers and small businesses.

Early Warning follows a hybrid work model to allow for a more collaborative working environment.

Candidates responding to this posting must independently possess the eligibility to work in the United States, for any employer, at the date of hire. This position is ineligible for employment Visa sponsorship.
Overall Purpose

This position serves as a data science team member in the company delivering leading edge machine learning and artificial intelligence concepts from start to finish in collaboration with senior technical and business leaders. This includes understanding the business problem/need, exploring and aggregating data, building and validating algorithms, and deploying completed models to deliver business results.

Essential Functions
Explore and aggregate data independently to uncover data anomalies that impact algorithm performance
End to end feature engineering - brainstorm, create, validate, down-select, etc.
Write production level code in a dynamic, start-up environment
Solve complex problems using terabyte size data sets
Apply of a variety of machine learning techniques to a business problem to arrive at optimal approach
Partner with Product and Engineering teams to solve problems and identify trends and opportunities
Explain and visualize results and algorithm performance to non-technical audiences
Support the company's commitment to protect the integrity and confidentiality of systems and data.
Support the company's commitment to protect the integrity and confidentiality of systems and data.

Minimum Qualifications
Bachelor’s Degree in Mathematics, Statistics, Computer Science, Operational Research or related field;
A minimum of 4 years data science, engineering, mathematics, or related work experience is required.
Experience developing data science pipelines & workflows in Python, R or equivalent programming language. Experience in writing and tuning SQL. Experience handling terabyte size datasets
Experience applying various machine learning techniques, and understanding the key parameters that affect their performance
Experience using ML libraries, such as scikit-learn, mllib, etc.
Experience using data visualization tools
Able to write production level code, which is well-written and explainable
Interest to do lots and lots of proof of concepts/rapid prototyping
Ability to effectively communicate findings from complex analyses to non-technical audiences.
Background and drug screen

Preferred Qualifications
Relevant experience in one or more of the following areas is
Experience in fraud analytics, particularly developing or validating fraud models at a bank or other financial institution.
Experience developing or validating banking risk models, including models used in Comprehensive Capital Analysis and Review (CCAR) stress testing or Current Expected Credit Losses (CECL) estimation.
Familiarity with model risk management practices, including development documentation, validation review, performance monitoring, and communicating model assumptions and limitations.
Experience with transaction or payment data, fraud patterns, and model evaluation for rare events, including tradeoffs between fraud detection and false positives.
PySpark experience and familiarity with scalable feature engineering and reproducible data science pipelines.
Knowledge of advanced machine learning algorithms and experience selecting methods appropriate to the business problem and data.
A track record of managing ambiguity, prioritizing work, and delivering proofs of concept or model development milestones, with timely communication of risks and dependencies.
Experience automating data science workflows or adopting shared modeling and feature engineering platforms.
PhD/MSc in Mathematics, Statistics, Computer Science, Operational Research or a related field.

Physical Requirements

Working conditions consist of a normal office environment. Work is primarily sedentary and requires extensive use of a computer and involves sitting for periods of approximately four hours. Work may require occasional standing, walking, kneeling, and reaching. Must be able to lift 10 pounds occasionally and/or negligible amount of force frequently. Requires visual acuity and dexterity to view, prepare, and manipulate documents and office equipment including personal computers. Requires the ability to communicate with internal and/or external customers.

Employee must be able to perform essential functions and physical requirements of position with or without reasonable accommodation.

The above job description is not intended to be an all-inclusive list of duties and standards of the position. Incumbents will follow instructions and perform other related duties as assigned by their supervisor.

Early Warning Services is an equal opportunity employer.

Chicago, IL in USD per year is: $101,000 - $152,000.
New York, NY/ San Francisco, CA in USD per year is: $122,000 - $162,000.
Additionally, candidates are eligible for a discretionary incentive plan and benefits.
Some of the Ways We Prioritize Your Health and Happiness
Healthcare Coverage – Competitive medical (PPO/HDHP), dental, and vision plans as well as company contributions to your Health Savings Account (HSA) or pre-tax savings through flexible spending accounts (FSA) for commuting, health & dependent care expenses.
401(k) Retirement Plan – Featuring a 100% Company Safe Harbor Match on your first 6% deferral immediately upon eligibility.
Paid Time Off – Flexible Time Off for Exempt (salaried) employees, as well as generous PTO for Non-Exempt (hourly) employees, plus 11 paid company holidays and a paid volunte...

About Early Warning®

Financial Services · 1,001-5,000 employees · headquartered in Scottsdale, Arizona