Data Scientist

We look for talented Data Scientists who can roll up their sleeves and have a direct impact on our company metrics. The performance of our models and experiments is seen astonishingly quickly; the learning loop is not measured in weeks or days but hours and minutes. We live in what might be the fastest model-learning playgrounds in the world. We have built an infrastructure that enables model deployment at both scale and speed. As data scientists, we sit alongside engineering colleagues who enable our models to deploy. Combine this with our growing variable set of hundreds of potential features (and growing! ), and this is a highly fertile environment for building, experimenting, refining, and achieving real impact from your models. If models fire, the bottom-line impact to our teams is immediate; you see the value of your work incredibly fast.

Responsibilities:

• 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, developing, and testing product experiments.
• You are expected to be a hands-on part of the role where you will also actively analyze 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 for the 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 the performance of a particular algorithm; from asking why something worked or why it did not work; and 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.

Requirements:

• Master's in a quantitative field such as computer science, electrical engineering, statistics, mathematics, operations research, economics, analytics, or data science. Or a bachelor's 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 understanding 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.