Expert Data Scientist
Job Title: Expert Data Scientist – Demand Planning & Forecasting (Machine Learning)
Project: Demand Planning ML Models Initiative
Role Level: Expert / Principal (Hands-on, Delivery-Focused)
Employment Type: Full-Time
Experience: 7+ Years
Job Location: Remote
Job Timing: Vietnam Time Zone
Role Overview
We are seeking an Expert Data Scientist with deep hands-on experience in machine learning–based demand forecasting to support the ANZ Demand Planning ML Models initiative.
This role requires a senior practitioner capable of independently designing, developing, and delivering enterprise-grade demand forecasting models. The ideal candidate will own forecasting solutions end-to-end and help improve business decision-making through highly accurate predictive models.
Key Responsibilities
Demand Planning & Forecasting (Core Focus)
Lead the design, development, and optimization of ML-based demand forecasting models.
Work extensively with time-series datasets related to sales, demand, and supply planning.
Apply and optimize statistical forecasting techniques and machine learning algorithms.
Perform advanced feature engineering incorporating seasonality, trends, promotions, pricing, holidays, and external drivers.
Conduct back-testing, validation, and scenario analysis to continuously improve forecast accuracy.
Data Science Leadership & Delivery
Own the end-to-end data science lifecycle, from data exploration and model development to deployment support.
Define, monitor, and optimize forecasting KPIs including MAPE, RMSE, and MAE.
Review, validate, and refine forecasting methodologies to ensure accuracy and scalability.
Clearly communicate model logic, assumptions, and insights to business and executive stakeholders.
Collaboration & Execution
Collaborate closely with Data Engineers, Analytics teams, and business stakeholders to operationalize forecasting models.
Support production deployment, monitoring, and model performance improvements.
Drive knowledge sharing and best practices in forecasting and machine learning across teams.
Required Experience
7+ years of experience in Data Science / Machine Learning.
5+ years of hands-on experience in demand forecasting or demand planning using machine learning.
Proven experience delivering production-grade or business-critical forecasting models.
Strong experience working with enterprise-scale time-series datasets.
Technical Skills
Advanced Python for Data Science and Machine Learning.
Strong expertise in time-series forecasting techniques (statistical and ML-based).
Experience with machine learning models such as regression, tree-based models, and ensemble methods.
Advanced feature engineering for demand and planning datasets.
Strong SQL and data manipulation capabilities.
Experience with forecast evaluation metrics (MAPE, RMSE, MAE).
Soft Skills
Strong ownership and accountability mindset.
Excellent analytical and problem-solving capabilities.
Ability to communicate complex technical concepts to non-technical stakeholders.
Comfortable working in fast-paced environments with tight delivery timelines.
- Locations
- Karachi, Pakistan, Lahore, Pakistan, Islamabad, Pakistan
- Remote status
- Fully Remote
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Work from anywhere
All positions marked "Global" at Bionic Talent are FULLY remote. We believe in a fully remote, interconnected world. Any job you apply to at Bionic Talent with a "Global" or "Fully Remote" tag will be fully remote; work from anywhere.
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Market beating compensation
We work with clients that value talent and ensure that our compensation is market-beating. We don't believe in market-competitive salaries; we believe in beating the market.
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No degrees needed
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About Bionic Talent
Bionic Talent is building a diverse workforce spanning 11 countries across five continents, speaking 14 languages.
With 13+ years of experience in remote work and team building, we understand what it takes to have a successful – and cost-effective overseas remote team.
Our proven approach is a smart way for companies looking for access to a global workforce. Using a global workforce allows savvy entrepreneurs to save on operating costs by hiring remotely.