Data Analyst I (ML) at AUTOPAY
Remote, Oregon, USA -
Full Time


Start Date

Immediate

Expiry Date

12 Sep, 25

Salary

90000.0

Posted On

13 Jun, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Data Science, Computer Science, Python, Power Bi, Data Analysis, Communication Skills, Sql, Completion, Mathematics, Relational Databases

Industry

Information Technology/IT

Description

This is a remote position. We are currently unable to consider candidates located in the following states: California, Oregon, New York, Illinois, Washington, District of Columbia or Outside the United States.

ABOUT US

AUTOPAY is an innovative FinTech company that is powering finance in the age of mobility. We function as a virtual Finance & Insurance office, finding our customers the perfect lender for their car loan or refinance. This means we’re able to solidify our customers’ auto loans prior to going to the dealership, and our marketplace of lenders ensures they get the lowest rate available.

SUMMARY

As a Data Analyst I (ML) at The Savings Group, you will support our data science initiatives by preparing data for analysis, conducting statistical evaluations, developing models under the guidance of senior team members, and assisting in implementing and monitoring machine learning solutions. This role is ideal for an early-career candidate looking to apply and grow their skills in analytics, machine learning, and AI within a collaborative environment. You will work closely with the Lead Data Scientist and other cross-functional teams to extract insights from data and contribute to projects that influence business outcomes.

SKILLS/REQUIREMENTS:

  • Degree (or near completion) in Data Science, Computer Science, Mathematics, Engineering, or a related field (master’s or higher degree preferred but not required).
  • 2-5 years of applicable work experience.
  • Hands-on experience with data analysis, statistical methods, and machine learning fundamentals.
  • Proficiency in Python (preferred) and relevant libraries (e.g., pandas, scikit-learn, matplotlib).
  • Familiarity with SQL and working with relational databases.
  • Knowledge of common data visualization tools (e.g., Tableau, Power BI, or open-source alternatives).
  • Strong analytical and problem-solving abilities, with attention to detail.
  • Excellent communication skills, both written and verbal.
  • Eagerness to learn and collaborate within a team setting.

MACHINES, TOOLS, EQUIPMENT AND WORK AIDS

Computer workstation and/or laptop, phone, copier and fax.
Job description statements are intended to describe the general nature and level of work being performed by employees assigned to this job title. They are not intended to be construed as an exhaustive list of all responsibilities, duties, and skills required.
The Savings Group (including all its subsidiaries: AUTOPAY, RateGenius Loan Services, Inc., and Innovative Funding Services dba Tresl) is an equal opportunity employer. With regard to hiring and promotions, qualified persons will not be denied employment opportunity based on race, color, national origin, religion, sex, sexual orientation, gender identity, marital status, age 40 and over, disability, military status, or genetic information. Any questions or concerns about our EEO policy should be directed to Human Resources
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Responsibilities
  • Assist with collecting, cleaning, and preprocessing data for use in modeling and analysis.
  • Collaborate with the Lead Data Scientist to explore business problems and identify opportunities for predictive modeling.
  • Build and evaluate basic machine learning models, support development of more complex algorithms.
  • Create visualizations and reports that clearly communicate findings to stakeholders.
  • Help monitor performance and accuracy of deployed models, support ongoing improvements and recalibration.
  • Conduct literature reviews and evaluate emerging ML/AI techniques and tools relevant to current projects.
  • Document methodologies, workflows, and findings for transparency and knowledge sharing.
  • Support cross-functional initiatives with data-driven insights and automation solutions
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