Data Science Contractor

ABOUT EARNIN

As one of the first pioneers of earned wage access, our passion at EarnIn is building products that deliver real-time financial flexibility for those with the unique needs of living paycheck to paycheck. Our community members access their earnings as they earn them, with options to spend, save, and grow their money without mandatory fees, interest rates, or credit checks.

We’re fortunate to have an incredibly experienced leadership team, combined with world-class funding partners like A16Z, Matrix Partners, DST, Ribbit Capital, and a very healthy core business with a tremendous runway. We’re growing fast and are excited to continue bringing world-class talent onboard to help shape the next chapter of our growth journey.

POSITION SUMMARY

As a Fintech company where Machine Learning (ML) is one of the critical drivers of growth, our operations highly rely on machine learning models, from business decisions to customer experiences. Therefore, building and deploying state-of-the-art machine learning systems to drive impact via our data capabilities is critical. To guarantee the success of machine learning systems, we need to formulate and experiment extensively and transform ML models to high-performance production-level code, including implementing sophisticated machine learning algorithms and robustness monitoring and system logging/alarming. This position will be based in India, ideally in Bengaluru, as part of our expanding site location.

WHAT YOU'LL DO

• Collaborate closely with other machine learning engineers and analysts to use and train state-of-the-art ML models for the product use cases assigned

• Understand the key metrics needed for such model evaluations

• Do in-depth data analysis to uncover insights and share them with partners

• Help in ensuring improvements in our current ML systems via model, data, or experimentation upgrades

• Gain hands-on experience with a wide array of technologies, including PyTorch, AWS, Kafka, data bricks etc

WHAT WE'RE LOOKING FOR

• Actively pursuing bachelor's, master or PhD in Computer Science, Information Technology, or a related field

• Demonstrate a keen interest in machine learning systems and eager to create impact via state-of-the-art data capabilities

• Strong understanding of statistical models, familiarity, and in-depth understanding of machine learning and deep learning algorithms such as clustering and classification.

• Proficient in at least one programming language and familiar with basic data structures and algorithms.

• Having big data experience and familiarity with distributed computation frameworks such as Hadoop, spark, hive etc. is desired but not mandatory

• Equipped with good verbal and written communication skills

• Detail-oriented, with a meticulous approach to problem-solving and project execution

• Committed to taking ownership of projects, conducting thorough investigations, and driving initiatives to conclusion

At EarnIn, we believe that the best way to build a financial system that works for everyday people is by hiring a team that represents our diverse community. Our team is diverse not only in background and experience but also in perspective. We celebrate our diversity and strive to create a culture of belonging. EarnIn does not unlawfully discriminate based on race, color, religion, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), gender identity, gender expression, national origin, ancestry, citizenship, age, physical or mental disability, legally protected medical condition, family care status, military or veteran status, marital status, registered domestic partner status, sexual orientation, genetic information, or any other basis protected by local, state, or federal laws. EarnIn is an E-Verify participant.

EarnIn does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or HR team.

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