Data Scientist - Sales Operations
Data Scientist - Sales Operations
Posted on :
06-04-2025
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1 Vacancy
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Job Description
Description
Enphase Energy is a global energy technology company and leading provider of solar battery and electric vehicle charging products. Founded in 2006 Enphase transformed the solar industry with our revolutionary microinverter technology which turns sunlight into a safe reliable resilient and scalable source of energy to power our lives. Today the Enphase Energy System helps people make use save and sell their own power. Enphase is also one of the fastest growing and innovative clean energy companies in the world with approximately 68 million products installed across more than 145 countries.
We are building teams that are designing developing and manufacturing nextgeneration energy technologies and our work environment is fastpaced fun and full of exciting new projects.
If you are passionate about advancing a more sustainable future this is the perfect time to join Enphase!
About the Role:
We are looking for a Data Scientist to join our Sales Operations team. The role will be based in our Bangalore India office reporting to the Manager Sales Operations. The position will be responsible for sourcing preparing and analysing large amounts of raw data to find patterns that will enable the company to better understand and optimize sales operations. The role will be responsible for the design implementation and maintenance of sophisticated analytics programs machine learning and/or statistical methods to prepare data for use in predictive and prescriptive modelling.
The ideal candidate needs to be highly analytical with a strong background and understanding in data analysis math and statistics. Critical thinking and problemsolving skills are essential. Experience with machinelearning and predictive analytics tools is required.
Key Responsibilities:
- Build predictive models and machinelearning algorithms for sales forecasting territory planning quota allocation and sales process optimization.
- Develop SKU level demand forecasts based on demand patterns business trends and advanced ML models/Predictive Analytics and drive forecast accuracy and operations metrics.
- Identify valuable data sources and automate collection processes.
- Undertake preprocessing of structured and unstructured data
- Analyse large amounts of information to discover trends and patterns related to sales operations.
- Develop models and algorithms to optimize sales resource allocation and sales performance.
- Analyse sales data to identify opportunities for process improvements and cost optimization.
- Collaborate with sales teams to understand their data needs and provide insights to drive better decisionmaking.
- Present information using data visualization techniques.
- Communicating and collaborating crossfunctionally throughout the entire company including sales finance supply planning pricing and PLM teams.
- Inventory levels forecasting and excess/shortage of FG & RM identification using ML models.
Make the existing sales ops processes scalable by identifying and implementing supply chain tools and automation.
Skills Required:
- 5 years of experience as a Data Scientist. Hands on experience in data manipulation using Python AWS SQL MS Excel is a must.
- Expert level proficiency in Python and Predictive Analytics (proficient in application of ML concepts and forecasting techniques to solve supply chain problems).
- Understanding of machinelearning and operations research.
- SFDC Oracle Cloud QlikView/Tableau or Incorta experience preferred.
- B.Tech/Masters required with knowledge of sales operations demand forecasting supply chain and experience in a product based industry.
- Very processfocused with excellent analytical and problemsolving skills and sound business judgment comfortable with datadriven decisionmaking.
- Ability to work productively and independently in a dynamic fast paced environment with exposure to executive staff.
- Bachelors BTECH/Masters (CGPA 7/70 with about 48 yrs of experience.
Employment Type
Full-Time
Department / Functional Area
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