Lead Data Scientist

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Lead Data Scientist

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.

Please visit Fractal | Intelligence for Imagination for more information about Fractal

Location: Bay Area, California

Position Overview:
We are looking for an experienced Lead Data Scientist within our Retail sector. In this role, will be a part of the Artificial Intelligence and Machine Learning, Algorithmic Decision-Making Team (AIML - ADM) and will be working in a fast-paced environment. S/He will be enabling clients to discover how they can leverage their data using advanced and sophisticated AI/ML algorithms.  

Key Responsibilities:

• Ability to lead the team to deliver AIML based solutions around a host of domains and problems, with hands on experience in Customer Segmentation & Targeting, Propensity Modelling, Churn Modelling, Lifetime Value Estimation, Forecasting, Recommender Systems, Modelling Response to Incentives, Marketing Mix Optimization, Price Optimization

• Should have led and delivered large scale Retail forecasting projects

• Thorough understanding of Promotions, Inventory Management, RGM, Base and uplift modelling

• Ability to understand a problem statement and implement analytical solutions & techniques independently with independently/proactively/thought-leadership.

• Work with stakeholders throughout the organization to identify opportunities for leveraging company/client data to drive business solutions.

Skills & Qualifications:

• 8-10 years in core Data Science & Machine Learning projects

• Expert level proficiency in Python, knowledge of Pyspark will be a plus

• Ability to create efficient solutions to complex problems. Strong skills in data-structures and ML algorithms.

• Experience of working on end-to-end data science pipeline: problem scoping, data gathering, EDA, modelling, insights, visualizations, monitoring and maintenance.

• Problem-solving: Ability to break the problem into small parts and applying relevant techniques to drive required outcomes.

• Advanced knowledge of machine learning, probability theory, statistics, and algorithms. You will be required to discuss and use various algorithms and approaches daily.

• Fast learner: ability to learn and pick up a new language/tool/ platform quickly.

• Conceptualize, design, and deliver high-quality solutions and insightful analysis.

• Conduct research and prototyping innovations; data and requirements gathering; solution scoping and architecture; consulting clients and clients facing teams on advanced statistical and machine learning problems.

• Collaborate and Coordinate with different functional teams (engineering and product development) to implement models and monitor outcomes.

Preferred Qualifications:

• Experience in one of the upcoming technologies like deep learning, NLP, image processing, recommender systems

• Experience of working in on one or more domains:

• Retail: Forecasting, pricing and promotion analytics, marketing analytics, trade promotions, supply chain management, Base & uplift

• Good grasp on databases including RDBMS, NoSQL, MongoDB etc.

Education:

B.E/B.Tech/M.Tech in Computer Science or related technical degree OR Equivalent 

Pay:

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions, including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.  The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled.  At Fractal, it is not typical for an indivi