Lead Data Scientist

3 weeks ago

bangalore, India Epsilon Full time
Job Description

Who we are looking for

At Epsilon, our people use real human insights to find solutions that empower some of the biggest brands in the world. Our team is now growing, and we are on the lookout for talented individuals who use descriptive, predictive and prescriptive analytics to impact consumer behaviour every day.

So, are you someone who enjoys leading conversations with influential stakeholders to help clients understand the value of their investments? Then you could be exactly who we are looking for.

 Apply today and be part of a creative, innovative and talented team who constantly share insights, solve problems and create the remarkable.

What you’ll do

The Client Analytics Group partners with both internal and external stakeholders, and data providers, leveraging machine learning and advanced statistical techniques to drive strategic thought and effective decision making. The Lead Data Scientist is responsible for applying and interpreting various statistical and data analysis techniques in support of a variety of analytic solutions.

Roles & Responsibilities:

  • Perform hands-on analysis on large volumes of customer’s first-party data and Epsilon’s third-party data to provide quality, statistically valid analysis and related output
  • Lead, manage, mentor and train a team of 2-5 Data Scientists (Junior and Senior) to implement the organizations vision and the roadmap with the help of the team members
  • Demonstrate proficient problem solving and data analysis, derived from modelling and statistical experience.
  • Take a proactive, independent role in analysis design, project planning and execution
  • Design and Implement Machine learning models using Spark ML and Python/Pyspark
  • QC data and results produced by junior and senior data scientists in the team
  • Create engaging and meaningful data visualizations / power point presentations of findings linked to clear client business impact.
  • Present statistical results to both technical and non-technical audience in a clear manner
  • Become proficient with Epsilon data assets
  • Own end to end implementations of Marketing machine learning models such as Customer Segmentation, Propensity models, CLTV etc.
  • Be an active learner - learn new and state of the art tools, technologies, algorithms and methodologies to be at the edge of data science learnings
  • Collaborate well with cross-functional internal teams as needed to drive results

Qualifications
  • 8-12 years of experience, with atleast 6+ years in core data science and machine learning
  • Good to have experience in managing a team of Data Scientists
  • Experience with large scale distributed databases and computing systems like Hadoop or Apache-spark using DataBricks
  • Strong passion for understanding key business problems, bringing together a team to understand data/ instrumentation needs and/or mine through data to unearth deep insights into customer experiences
  • Proven capability to deliver end-to-end analyses by asking the right questions, extracting data, and building predictive models to ensure actionable results.
  • Excellent communication & interpersonal skills with an ability to communicate ideas.
  • Bachelor’s degree, or higher, in a quantitative discipline (e.g., Engineering, Statistics, Economics, Mathematics, Marketing Analytics) or significant relevant coursework
  • Strong experience with Python/Pyspark programming language
  • Experience working with SQL is a plus
  • Must have hands-on experience with machine learning and statistical techniques like Logistic regression, Decision trees, Random Forest, K-means clustering etc.
  • Experience with Marketing machine learning models is a plus
  • Deep experience in machine learning with Spark, MLlib is good to have

Additional Information

Epsilon is committed to promoting diversity, inclusion, and equal employment opportunities by using reasonable efforts to attract, recruit, engage and retain qualified individuals of all ethnicities and backgrounds, including, but not limited to, women, people of color, LGBTQ individuals, people with disabilities and any other underrepresented groups, traits or characteristics.

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