Equifax, Inc.

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Data Scientist (Finance)



Equifax Enterprise Innovation Office is seeking a Data Scientist who can integrate diverse big data assets into analytical solutions under management guidance to solve business problems. With a strong heritage of innovation and leadership, we leverage our unique data, advanced analytics, and proprietary technology to enrich the performance of businesses and the lives of consumers.

Qualified candidates will have a passion for mathematics, statistics, AI/Machine learning, data gathering, and experience in financial modeling. The ideal candidate will combine the skills to contribute to new prototypes with the creativity and thoroughness to ask and answer questions about the data, and to apply big data analytics/modeling techniques effectively. The ideal candidate will be passionate about exploring how models/techniques work and has a desire to understand complex data patterns.

Equifax has a hybrid work schedule that allows for 2 days of remote work (Monday and Friday), with 3 days onsite (Tuesday, Wednesday, Thursday) every week.

This role reports to our office in Alpharetta, Georgia or midtown ATL (OAC).

This position does not offer immigration sponsorship (current or future) including F-1 STEM OPT extension support.

This is a direct-hire role and is not open to C2C or vendors.

What You'll Do:

  • Contribute as part of the Data Science Lab team, collaborating with internal clients in various phases of prototype development and deployment.
  • Explore and apply innovative data solutions (in distributed cloud computing constrained and unconstrained optimization) to solve real market problems.
  • Leverage expertise in data structures, analytics, algorithms/models, and computer science fundamentals to prepare, analyze, and develop robust, deployable solutions
  • Design, write, and optimize complex SQL queries for data extraction, transformation, and analysis, often dealing with large datasets.
  • Assist in the design, development, and implementation of NLP and LLM solutions, including text classification, summarization, and NER (Name Entity Recognition), leveraging state-of-the-art embedding models like Gemini and BERT.
  • Contribute to the development and implementation of solutions for efficient processing of large-scale data using distributed systems.
  • Data Quality & Labeling: Demonstrate a commitment to data integrity by patiently and meticulously performing essential tasks such as data labeling, classification verification, and anomaly detection.
  • Contribute to developing analytical approaches to meet business requirements; this involves translating requests into use cases, test cases, preparation of training data sets, and iterative product development.
  • Collaborate with key stakeholders to understand their needs and contribute to developing advanced solutions or improving existing solutions around big data and advanced analytics.
  • Remain current on new developments in AI/Machine Learning, distributed algorithms, Big Data, Predictive Analytics, and Cloud Technology.

What experience you'll Need:
  • Master's degree in Mathematics, Statistics, Data Science, Physics, Computer Science, Operations Research, Economics, Engineering or related quantitative field, or a Bachelor's degree with equivalent relevant experience.
  • 2+ years of experience applying predictive analytics and modeling to solve business problems; this can include internships and/or a Master's Degree.
  • 1-3 years of experience with Python, Tensorflow, SQL (solid skills and scripting experience), and Spark with experience in data manipulation libraries (e.g., Pandas, Dask, Spark DataFrames).
  • Foundational understanding of algorithm time and space complexity, and an ability to apply this knowledge to develop efficient data science solutions.
  • Strong problem-solving skills, with the ability to navigate ambiguity and deliver results in a fast-paced environment.

What could set you apart:
  • Experience with large-scale data processing in distributed environments.
  • Strong communication skills of analytical results to technical and non-technical audiences alike.
  • Experience working on big data platforms (e.g., Google Cloud, AWS, Snowflake, Hadoop) a plus.
  • Familiarity or exposure to NLP (Natural Language Processing), LLMs (Large Language Models) and/or Generative AI.
  • Agile development including Scrum.

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