Data Scientist

Inmobi

2 - 4 years

50 - 60 Lacs

bengaluru

Posted:19 hours ago| Platform:

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Skills Required

computer science accessories san cloud computing operations research machine learning conceptualization genetics analytics python

Work Mode

Work from Office

Job Type

Full Time

Job Description

Our scientists are expected to develop deep expertise and experience in Ad Tech, AI/ML, and Data Science, particularly at scale. Familiarity with big data processing and cloud computing will be critical to succeed in this environment.
In addition to possessing a mathematical aptitude (Statistics, Probability Theory, Algorithms, Foundations of Machine Learning), they need to be competent with data science languages and tools, such as Python or Apache Spark; which will enable them to design scalable solutions for our advertising products, implement proof-of-concept, and evaluate them offline and online. They will also need to work with other engineers to take these solutions to live production and drive real business value.
Most importantly, we look for a passion to investigate and learn about the world from data, to ask interesting and provocative questions, and be driven to put real models into production that drive real business value.
We are open to diverse academic backgrounds, providing an intent to think and problem-solve like a data scientist. Our team includes engineers, mathematicians, computer scientists, physicists, economists and social scientists a rock-star data scientist can come from any academic field.

Required:

Master s in a quantitative field such as Computer Science, Electrical Engineering, Statistics, Mathematics, Operations Research or Economics, Analytics, Data Science. Or Bachelors from a reputed College with additional experience.
2 to 4 years of work experience in a quantitative field, with model building and validation experience.
Ad tech related industry experience is a plus. There, you would have applied algorithms and techniques from Machine Learning, Deep Learning, and Statistics or other domains in solving real world problems and understand the practical issues of using these algorithms especially on large datasets.
Comfortable with software programming and statistical platforms such as R, Python etc. including visualization tools.
Comfortable with the big data ecosystem and Apache Spark. Familiarity with Microsoft Azure, AWS, or Google Cloud/Vertex AI will be a bonus.
Comfortable collaborating with cross-functional teams.
Excellent technical and business communication skills and should know how to present technical ideas in a simple manner to business counterparts.
Possess a high degree of curiosity and ability to rapidly learn new areas.

What will you do

You will be responsible for one or more data science efforts for inMobi DSP, a gold mine that is being explored for riches. This involves project ideation and conceptualization, solution design, measurement and solution iteration, coaching, deployment and post deployment management.
This will also include designing, development, testing of product experiments.
You are expected to be a hands-on part of the role where you will also actively analyse data, design and develop models, and solve problems with the rest of the team.
You will learn how to design and build models for specific business problems. Even before that, you will be responsible for identifying the problem areas where AI can be applied to best business impact. You will learn to start a model design by anchoring in the business context and end user needs. You will learn how to connect model impact with real and measurable business impact.
You will work in a multi-functional team environment. You will collaborate and benefit from the skills of a diverse group of individuals from teams such as engineering, product, business, campaign management and creative development.
You will have the opportunity to experiment with multiple algorithms. Enduring learning comes from building, launching and reviewing performance of a particular algorithm; from asking why something worked or why it did not work; from asking how to tailor techniques to fit the problem at hand. We have an environment that makes this possible at speed.
Importantly, you will learn to become creative in designing models to be successful. Model design is not one-size-fits-all. Our models need to fit our particular problems and be modified to perform. Tougher problems require layers of models, and feedback mechanisms in a dynamic environment such as ours.

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