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Job Location: Bangalore
About Razorpay
In 2014, what started as India’s first payment gateway built for Startups is
now one of India’s youngest FinTech unicorns with a $3 Bn valuation and the
single best destination for all things payments and business banking. Razorpay
offers an integrated platform for all things payments and business banking,
helping millions of small and big businesses simplify and accelerate every
aspect of their financial journey.
Today, Razorpay is a 1400 fun bunch of spirited, analytical, and ambitious
folks building the first-of-its-kind technologies for the fintech ecosystem. We
are a bunch of hungry mavericks here to empower 10Mn businesses in their
digital transformation and enhance the payment experience of over 500 million
end consumers in the next twelve months.
And we are just getting started, Would you want to join this club of
thinkers, innovators, dreamers, and challengers?
Job Role: Senior DS/MLE
Job Summary
We are looking for a savvy Data Scientist/ ML Engineer to join our growing
team. The hire will be responsible for expanding and optimizing our data
pipeline architecture and building SOTA models for several use-cases. Problems
examples – Risk engines, Recommendation systems, NER for address, CV for
auto-verifications, and much more.
Your Opportunity
As part of the DS team @Razorpay, you’ll have the opportunity to work with
some of the smartest engineers/architects/data scientists and will have the
opportunity to solve one of the most critical problems for Razorpay. You will
grow as a mentor while being hands-on for all programing requirements, and make
important business decisions.
Responsibilities
- You will work with huge volumes of data to solve/enable solving real-world
ML problems that are productionized into our ML platform to be consumed by
downstream applications OR become product features OR stand-alone products.
- You will combine data sciences depth, programming expertise and
mathematical understanding of techniques to deliver state-of-the-art ML
solutions for problem solving across the enterprise.
- You will demonstrate a product mindset and look to deliver reusable
components that can be deployed as or into ML services/productized solutions.
- You will partner closely with engineering, product management, analytics &
growth teams from across org to deliver outstanding value to stakeholders and
our products
- Demonstrate strong program management expertise to be able to multitask
effectively
- Mentor junior data scientists effectively to deliver success.
- Running end-end data sciences projects from a conceptualization to
completion
- Able to present to stakeholders effectively about the findings & also
iterate on the business problem definition & experimentation
- Be a Team Player. Apart from working cross-functionally effectively,
contribute to peer reviews, team learning, meet product objectives and develop
best practices for the organization
Your Qualifications
- 4-6 years of experience doing predictive modelling and machine learning in
a production setting
- Masters or PhD in Operations Research, Applied Math, Computer Science,
Engineering, or related quantitative subject
- Strong expertise and mathematical understanding of one or more of the
following areas is required:
- Classic Machine learning techniques including regression, classification,
similarity scoring and other predictive methods
- Bayesian Methods and techniques
- Experience in developing AI solutions for any of the following domains:
Computer vision, Image processing, Natural Language Processing (NLP) or
Forecasting.
- ML Explainability methods and evaluation of ML algorithm performance.
- ML experiment design and tracking, A/B/N testing with strong hold on
statistics.
- Strong analytical abilities, ability to define relevant measurements /
metrics for a given problem
- Strong technical programming ability with an emphasis on Python, Golang is
a plus.
- Experience working with models in production environments
- Experience with deep learning toolkits and frameworks
- Experience with SQL and/or Big Data Tools (expert level knowledge of spark,
hive)
- Experience with AWS tech-stack is a plus.
- Intelligent but practical; you want to get things done and love simplicity
- Excellent written and verbal communication skills
- Comfortable interacting with technical and non-technical audiences
including business and tech leadership
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