5 best things about this role:

1. Being a part of India’s fastest-growing media-tech company
2. Grow as an ‘intrapreneur’ by independently handling projects
3. Own the vision of a particular product or set of products, including the goals and roadmaps for the team
4. Collaborate on individual and shared creative projects
5. Work on a range of deliverables by combining engineering, design, and data science to create strategies to reach these shared goals

Measures of success –

In three months:

• Achieve a clear understanding of the approach, process, expectations, and timelines.
• Work on complex digital data, analyze and convert it into measurable insights
• Work closely under the leadership of senior management on strategic projects

 In six months:

• Drive and work closely with multiple teams and identify a relevant clear set of metrics that inform the success of products

 In twelve months:

• Independently lead projects to build tech products for the future keeping in mind data integration, data-led research and emerging technologies.

Desired Skills and experience: 

• Advanced degree in Statistics, Computer Science, or a relevant quantitative field
• Strong programming skills in Python using a deep learning framework (Tensorflow/PyTorch)
• Minimum 6 Years of experience in Data Science/Machine Learning role
• Proven track record of leveraging massive amounts of data to drive product innovation
• Deep statistical skills utilized in A/B testing, analyzing observational data, and modeling
• Experience with distributed analytic processing technologies (Spark, Presto, Hive)
• Exceptional interpersonal and communication skills
• Deep product sense

 Tasks you get to solve: 

• Lead the design, analysis, and interpretation of experiments that shape decision-making around personalization algorithms
• Proactively perform data exploration to understand user behavior and identify opportunities for improving recommendation algorithms
• Drive innovation by developing new experimentation methodologies, statistical techniques, and causal-inference approaches that could sharpen our product decision-making process