About YipitData:YipitData is the leading market research and analytics firm for the disruptive economy and recently raised up to $475M from The Carlyle Group at a valuation over $1B.We analyze billions of alternative data points every day to provide accurate, detailed insights on ridesharing, e-commerce marketplaces, payments, and more. Our on-demand insights team uses proprietary technology to identify, license, clean, and analyze the data many of the worlds largest investment funds and corporations depend on.For three years and counting, we have been recognized as one of Incs Best Workplaces. We are a fast-growing technology company backed by The Carlyle Group and Norwest Venture Partners. Our offices are located in NYC, Austin, Miami, Denver, Mountain View, Seattle, Hong Kong, Shanghai, Beijing, Guangzhou, and Singapore. We cultivate a people-centric culture focused on mastery, ownership, and transparency. Why You Should Apply NOW:We are seeking a highly skilled and detail-oriented Data Operations Analyst to play a crucial role in developing custom product attribution solutions based on unique customer needs. This position requires a deep understanding of consumer product data, a strong combination of technical abilities, and a proactive approach to delivering high-quality results. The Data Operations Analyst will work closely with our corporate retail and brand customers, who may have different approaches to organizing and structuring their categories. Your primary responsibility will be to map products to customer requirements using a blend of manual tagging and machine-learning tools. About The Role:The Data Operations Analyst plays a critical role in developing custom product attribution based on unique customer needs. Each of our corporate retail and brand customers thinks about the structure of their categories in slightly different ways, and the Data Operations Analyst will execute the mapping of products to their requirements using a combination of manual tagging and machine learning tools. Interpretation and execution design require strong judgment. The ideal candidate should bring a combination of technical skills, knowledge of consumer product data, strong attention to detail, and accountability to plan and deliver projects on time with a high degree of accuracy. This is a fully-remote opportunity based in India:
During onboarding and training period, we expect several hours of overlap with US time zones.
Afterward, hires should be available for meetings and check-ins with their US managers and colleagues; however, outside of these specific times, standard work hours can be flexible.
As Our Data Operations Analyst, You Will:
Work with corporate retail and brand customers to understand their category structures and product attribution requirements.
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Execute the mapping of products to customers' category needs, utilizing both manual tagging and machine learning tools.
Apply strong judgment and interpretation skills to ensure that data mapping aligns with customer specifications and business goals.
Collaborate with cross-functional teams to plan and deliver projects on time, ensuring high levels of accuracy and precision.
Continuously monitor and improve product attribution processes to increase efficiency and quality.
Leverage technical skills to enhance existing processes and tools that support data operations.
Maintain strong attention to detail and ensure accuracy in every aspect of the data mapping process.
Take accountability for successfully executing projects, including meeting deadlines and client expectations.
You Are Likely To Succeed If You Have:
1-2yrs of experience developing a strong technical background, including applied Python, Pyspark, and/or SQL skills
RegEx experience is not mandatory but would be considered a nice-to-have skill
Proficiency in data analysis, tagging systems, and machine learning tools.
Knowledge of consumer product data and experience working with retail or brand data.
Exceptional attention to detail, with a focus on delivering high-quality, accurate results.
Excellent problem-solving and critical-thinking skills.
The ability to manage multiple tasks and projects while maintaining a high degree of accuracy.
Strong communication and collaboration skills to work with internal teams and external clients.
Proven ability to execute projects efficiently and meet deadlines.
Preferred Skills for This Position Include:
Experience with data management platforms, product attribution systems, or machine learning tools.
Familiarity with data mapping, tagging, and categorization practices.If you are a proactive, technically skilled individual with a passion for data and product attribution, we invite you to join our dynamic team! Apply today to help shape the future of data operations for our retail and brand customers.
What We Offer:Our compensation package includes comprehensive benefits, perks, and a competitive salary:
We care about your personal life and we mean it. We offer vacation time, parental leave, team events, learning reimbursement, and more!
Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust.
The annual salary for this position is anticipated to be 16,60,000 - 20,75,000 (INR). The final offer may be determined by a number of factors, including, but not limited to, the applicant's experience, knowledge, skills, and abilities, as well as internal team benchmarks. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal-opportunity employer.
Who can apply
Only those candidates can apply who
have minimum 1 years of experience
Salary
Probation:
Duration:
Salary during probation: After probation:
Annual CTC: Competitive salary
Number of openings
1 Editor’s note
Information above is Internshala's interpretation and paraphrasing of what we found on the shared link.
