Data Scientist: Lead Production ML Ad Tech

The Impact You'll Make:

• You will be responsible for leading the data science efforts for one of the biggest in-app programmatic exchange in the world. 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 will need to guide the team in practical experiments, product design, model development and model evaluation. It is vital to be agile and iterate fast across experiment to deliver go-to-market ready products.
• You are expected to be a hands-on part of the role where you will also actively analyse data, design and develop models, and problem-solve solutions with the rest of the team.
• Additionally, stakeholder management is needed. It will involve being the interface with internal stakeholders such as our Product, Engineering, Data, Infrastructure, and Business teams.
• Our team strives for thought leadership in the sector. We encourage and support all team members to write blogs, commentary and case studies published on the InMobi blog.
• We also support team members across our ML/AI team to speak at industry conferences and represent InMobi’s work.
• 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  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. 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.
• We are a company that innovates and demonstrates our thought leadership to the world, whether in products, research papers or conferences – there are many opportunities for you to shine.

The Experience You'll Need:

• The core foundation we look for is an aptitude with Mathematics, Statistics, Algorithms, Optimization and a competent ability in coding and with data science languages and tools, such as Python or Apache Spark. 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.
• Basics of big data processing and cloud computing will be critical to succeed in this environment.
• 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, statisticians, physicists, economists and social scientists – a rock-star data scientist can come from any academic field. We are looking for a Staff level Data Scientist, but depending on the experience we may hire at a higher or lower level.

Requirements:

• Master’s in a quantitative field such as Computer Science, Statistics, Electrical Engineering, Statistics, Mathematics, Operations Research or Economics, Analytics, Data Science. Ph.D. is a huge plus.
• Depending on the level we are looking for experience in the Ad Tech Industry working in Data Science teams. You would have applied algorithms and techniques from Machine Learning, Statistics, Time Series or other domains in solving real world problems and understand the practical issues…