Sr. Data Scientist

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.

Positions located in Scottsdale, San Francisco, Chicago, or New York follow 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 senior data science team member in the company developing techniques to identify entities at risk in a moment in time in a multifaceted, high-volume, high-throughput data environment. This position requires extensive background and knowledge in machine learning. Previous experience in analyzing large datasets and developing data-driven statistical models is required.

Essential Functions

• Identifies, experiments with, and develops appropriate machine learning techniques to extract the value in data from various sources to solve valuable business problems
• Assists with the development of complex consumer profiles which are used for model training and real-time scoring
• Take the key role in the development and implementation of product-prototype models
• Assesses overall performance, stability, and effectiveness of analytically derived models.
• Documents and presents model process and model performance
• Collaborates with software engineers to define statistical components for unit testing and acceptance testing
• Remains fluent with emerging technologies and methodologies, shares knowledge, and serves as subject expert and a mentor to junior data scientist
• Translates high level business objectives into quantifiable analysis tasks.  Identifies and recommends new modeling and analytics opportunities.
• Support the company's commitment to protect the integrity and confidentiality of systems and data.

Minimum Qualifications

• Bachelor’s Degree in Mathematics, Statistics, Machine Learning, Computer Science or related field.
• 7 years working experience in predictive modeling, optimization, and machine learning (or equivalent education and experience).
• Advanced experience in data mining with a range of advanced technical tools (Python, R, Hadoop, Hive, SQL, Java, Spark, etc.) for timely manipulation of large data sets.
• Experience with various machine learning methods including classification/tree, SVM and ensemble approaches
• Experience in utilizing a wide variety of statistical modeling techniques.
• Experience with understanding business requirement and translating into an analytics design
• Effective communication skills
• Proven ability to coordinate or lead data scientists on projects
• Background and drug screen

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.

Phoenix, AZ in USD per year is: $120,000 - $145,000.
San Francisco, CA in USD per year is: $125,000 - $150,000.

Additionally, candidates are eligible for a discretionary incentive plan and benefits.

This pay scale is subject to change and is not necessarily reflective of actual compensation that may be earned, nor a promise of any specific pay for any specific candidate, which is always dependent on legitimate factors considered at the time of job offer. Early Warning Services takes into consideration a variety of factors when determining a competitive salary offer, including, but not limited to, the job scope, market rates and geographic location of a position, candidate’s education, experience, training, and specialized skills or certification(s) in relation to the job requirements and compared with internal equity (peers). The business actively supports and reviews wage equity to ensure that pay decisions are not based on gender, race, national origin, or any other protected classes

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