- This involves project ideation and conceptualization, solution design, measurement, solution iteration, coaching, deployment, and post-deployment management.

- This will also include designing, developing, and 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 the 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.

Requirements :

- Master's in a quantitative field such as Computer Science, Electrical Engineering, Statistics, Mathematics, Operations Research or Economics, Analytics, or Data Science. Or a Bachelor's from a reputed College with additional experience.

- 6 to 9 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 the ability to rapidly learn new areas.