lead data science.

• While designing solutions, keep an eye on implementation & application of Machine Learning
algorithms being built.
• Work with different teams to collate data from HubSpot &/or Salesforce to build such
solutions.
• Design experiments where required and frame means to harvest data generated from such
experiments with an objective to use them in exercises.
• Be technically good in building models & data management.
• Has Strong communication skills and is able to articulate and present ideas or work to Senior
Management and key stakeholders, as well as colleagues and support functions.
• Take lead in executing tasks on technical front and mentor / guide junior members of the team
on the same.
• Be a subject matter expert on Customer Lifetime Value works and preferably have some
knowledge of Market-Mix modelling and Customer Acquisition & Retention.
o Work on the subject at both a portfolio level as well as at a customer level with anintent to using them in conjunction with other streams like Acquisition & Market-Mix
works.
o Has a good understanding of economics behind Lifetime Value calculations and
acquisition & retention.

• Manage team on day-to-day activities including planning & allocating tasks as required in
collaboration with senior leaders.

Required Profile
• Has worked extensively on CLTV challenges using data science, preferably for the Financial
Services industry.
• Has had exposure to collaborating with teams distributed across geographies & time-zones.
• Has worked on CLTV at both a portfolio level as well as at a customer level.
• Has good exposure to HubSpot, Salesforce environments.

• Be proficient in different modelling techniques including but not limited to Linear & Logistic
regression, Random Forest, XGBoost, Chaid trees, survival modelling etc. using Python or
other tools as may be required.
• Has had at least 2-4 years’ experience in managing teams with a total experience of 5-8 years.
• Has a degree in engineering from an institution of repute – preferably from the IITs or NITs or
has a degree in Economics or statistics from an institution of repute.