Objectives
Job Description
- Predictive Modelling & Business Forecasting
- Design, build, and deploy predictive models and forecasting pipelines for core business operations: revenue forecasting, project profitability prediction, resource utilization optimization, and cost modelling.
- Define model objectives, success metrics, and validation frameworks aligned with business KPIs.
- Translate complex model outputs into clear, actionable business recommendations for leadership and cross-functional teams.
- Continuously improve model accuracy and relevance through monitoring, retraining, and feedback loops.
- Data Modelling & Feature Engineering
- Architect data models and feature stores that support scalable machine learning and analytics use cases.
- Work with BI and Engineering teams to ensure clean, reliable, and well-documented data pipelines feed into modelling workflows.
- Define and maintain metric definitions, data dictionaries, and source-of-truth standards for predictive use cases.
- Perform data profiling, anomaly detection, and quality validation to ensure model inputs are robust.
- AI-First Strategy & Use Case Development
- Partner with CTO and business leaders to identify and prioritize high-impact AI/ML use cases across operations, marketing performance, and internal platform capabilities.
- Build the roadmap for embedding prediction and intelligent automation into PMAX's workflows and tools.
- Evaluate and recommend emerging technologies (LLMs, GenAI, AutoML) where they create genuine business value — not innovation for its own sake.
- Establish experimentation frameworks (A/B testing, offline evaluation, champion-challenger) to validate model impact before production deployment.
- Team Building & Data Science Practice Leadership
- Recruit, coach, and manage a team of 2–4 Data Scientists and/or ML Engineers.
- Set quality standards for the DS practice: code review, model documentation, reproducibility, and deployment discipline.
- Foster a culture of rigour, curiosity, and business-first thinking within the team.
- Define career development paths and mentoring practices for team members.
- Cross-Functional Collaboration & Stakeholder Enablement
- Work closely with BI to ensure predictive insights are surfaced in dashboards and decision workflows (not siloed in notebooks).
- Partner with Engineering to productionize models — ensuring reliability, monitoring, and scalability.
- Collaborate with Finance, Operations, Account, and Media teams to deeply understand their planning and decision-making needs.
- Present findings and recommendations in clear, business-oriented language to non-technical stakeholders and senior leadership.
Job Requirements
Core Competencies
- 5–8 years of experience in Data Science, Machine Learning, or Applied Analytics roles, with at least 1–2 years in a team lead or management capacity.
- Strong foundation in statistical modelling, time-series forecasting, and machine learning (regression, classification, clustering, ensemble methods).
- Proficiency in Python (pandas, scikit-learn, statsmodels, XGBoost/LightGBM); experience with deep learning frameworks is a plus.
- Strong SQL skills; comfortable working with complex, multi-source datasets.
- Hands-on experience building and deploying models in production or semi-production environments (not just research/notebooks).
- Experience with data modelling, feature engineering, and pipeline design for ML use cases.
Business & Domain Knowledge
- Strong understanding of business operations data: revenue, cost, profitability, resource allocation, project performance.
- Ability to connect operational data with marketing and commercial outcomes to deliver end-to-end business intelligence.
- Experience working with ERP, CRM, financial, or operational datasets is a strong plus.
- Familiarity with digital marketing metrics and performance data (paid media, funnel analytics, attribution) is an advantage.
Leadership & Mindset
- Proven ability to recruit, mentor, and develop a small technical team.
- High ownership and accountability — treats model impact as the goal, not model complexity.
- Pragmatic and outcome-driven; able to balance scientific rigour with business urgency.
- Strong communication skills — can explain complex concepts simply and influence cross-functional stakeholders.
- Curious and continuously learning; open to adopting AI tools (including GenAI) to improve team productivity.
Nice-to-have
- Experience with MLOps tooling (MLflow, Airflow, cloud ML services).
- Exposure to LLM/GenAI applications in business contexts.
- Experience in agency, MarTech, e-commerce, or performance-driven environments.
- Background in forecasting at scale (demand planning, financial forecasting, marketing mix modelling).
PMAX-ER IDENTIFICATION
- Client Impact
- Innovation
- People Development
- Integrity
- Teamwork and Fun
- Extreme Ownership
Benefits
Competitive salary with quarterly and annual bonuses, and a 13th-month salary
Flexible working hours with 4 remote working days per month and 15 annual leave days
Comprehensive salary-based insurance (SHUI)
Annual regular health check-ups and PTI health care insurance for all employees
Providing/laptop allowance or supporting laptop purchase costs for individuals
Internal training and career development opportunities, and sponsorship for external L&D budgets
Quarterly team bonding budgets, snack time for team bonding
Gifts & Awards for Quarters, Years, and special occasions (birthdays, New Year, etc.)
Holiday activities; Company trips; Year-end parties; Company birthday; Cultural Day; Quarterly town halls
Job Summary
Other welfare benefits for employees.
Year Of Experience
5 years+
Job Level
Manager
Report Line
CTO
Peer
Board of Management (BoM)
Subordinate
1–4 (Data Scientists / Data Modelling)
Salary Range
Negotiable
Hiring Purpose
New Hire
Working Location:
7th Floor, Tuong Viet Building 95 Cach Mang Thang 8, Dist. 1, Ho Chi Minh City, Vietnam
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