Job Description

Data Science Manager/Expert (hands-on IC role)

Note : This is a full time work from office role.



- This is a very hands-on IC role and requires (apart from other managerial duties) to code, build and deploy models and model monitoring mechanisms

- Extensive cross-organization collaboration playing a crucial role in interfacing with various business, IT, Risk and internal DS functions and helping them with identifying opportunities to leverage data assets to build decision support models and tools

- Work with Telesales, Digital, Marketing & CRM teams on defining customer segments basis varied criteria; help identify segments for up/cross sell, for different marketing and digital campaigns

- Build advanced analytical models at scale spanning different areas - targeting and segmentation models, MMM, RFM, attribution models, next best offer, next best action, voice call (and meta data) analysis, propensity to convert, customer life-time value (CLTV), etc.

- Utilize internal, 3rd party and social data to generate holistic insights

- Gain full understanding of the organization\'s data assets to identify what can be leveraged

- Come up with innovative ideas to leverage data and build PoCs, PoVs, gain buy-in from business functions and take PoCs and PoVs to production at scale

- Translate use cases to analytics agenda, including data, approach and validation strategy

- Develop best practices and model development, deployment and monitoring frameworks and guide data science and data engineering elements of AI & data science projects

- Mentoring junior staff regarding technical solutions and data analysis techniques.

- Drive full and thorough documentation of existing and future analytical models, deployment plans, monitoring mechanisms

Requirements

- University degree in mathematics/statistics/engineering/IT sciences from top level university. Masters degree is a plus

- 4-8 years of work experience in data analytics/data science

- Hands on experience with SQL,Python, Azure ML/GCP etc. Must have the ability/experience to navigate easily on command line interfaces on Linux systems

- In-depth understanding of modern machine learning techniques (supervised and un-supervised learning) and their mathematical underpinnings

- Deep experience working with real world NLP/NLU problems and Knowledge Management / Representation

- Experience working with Knowledge Graphs, Bayesian networks, Deep belief networks, Q&A Systems (paraphrasing, multi-intent detection, long term context retention) will be an added advantage.

- Experience working with latest state-of-the-art Deep Learning and Machine Learning approaches along with others (like Sequence Models - BERT, ELMo, Graphical Models etc.)

- Ability to design, deploy and execute end to end ML/DL models at scale on on-prem servers in an ML-Ops driven environment

- Experience with big data technologies like Hadoop ecosystem together with Spark, Google Big Query, PySpark, etc.

- Advanced (hands-on) knowledge of Python based libraries like seaborn/plotly/bokeh or Tableau/another visualization tool

- Interpret results and drawing key business insights from advanced quantitative analyses

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