Overview
Skills
Job Details
KPI Partners, A global consulting firm focused on strategy, technology, and digital transformation. We help companies tackle their most ambitious projects and build new capabilities. We provide solutions in Cloud, Data, Application Development & BI spaces.
We enable your growth
At KPI, you can become who you want to be and learn skills that will take you further in your career
Continuously upgrade yourself
Develop as a future leader
Drive cloud enablement around the world
Engineering Excellence
Enhance your engineering expertise with our unique approach
This program gives engineers the opportunity to excel in product and software engineering by learning our industry-leading practices, tools, and technologies to build excellence by enhancing their competencies and skills
Visit to Know more :
Title: Data Scientist
Location: Fremont, CA (Hybrid) / Remote
Work Hours: PST Zone (8 AM - 5 PM)
Duration: 12 Months
Key Skills: Nixtla, pytorch-forecasting, scikit-learn
Nice to Have: Python, Azure, Databricks, ARIMA
Who We re Looking For
- Proficiency in python with at least 4 years of experience developing data science applications and/or software development.
- 3+ years of demonstrated experience in applied forecasting, preferably in demand or revenue forecasting contexts.
- Strong understanding of time series modeling techniques including ARIMA, exponential smoothing, Prophet, and deep learning-based models.
- Proficiency in Python and experience with data manipulation libraries like pandas and numpy.
- Experience working with cloud platforms (e.g., Databricks, Azure) and MLOps tools is a plus.
- Strong communication skills and ability to work cross-functionally in a fast-paced environment.
- Bachelor s or Master s degree in Data Science, Statistics, Computer Science, or a related field.
Preferred Qualifications
- Hands-on experience with Nixtla libraries (e.g., statsforecast, mlforecast), pytorch-forecasting, and scikit-learn.
- Experience with hierarchical or multi-level forecasting.
- Familiarity with probabilistic forecasting and uncertainty quantification.
- Exposure to business domains such as supply chain, finance, or sales operations.
- Contributions to open-source forecasting libraries or research publications in time series.