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.
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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.
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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.
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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.
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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).
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PMAX-ER IDENTIFICATION

• Client Impact
• Innovation
• People Development
• Integrity
• Teamwork and Fun
• Extreme Ownership
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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