Data Scientist

Tecforte; Selangor; Hybrid; MYR 120,000 - 180,000; Full time

Job summary

Tecforte in Malaysia is seeking a Data Scientist who combines curiosity, analytical rigor and business sense to turn real-world data into actionable intelligence for security and operations.

You will collaborate with AI engineers, software developers, product teams and industry specialists to build models, evaluate performance and translate results into business insights.

Qualifications

• Degree in Data Science, Computer Science, AI, Statistics, Mathematics, Engineering or a related discipline.

Responsibilities

• Discover Intelligence from Real-World Data.
• Develop AI & Data Science Models.
• Research Industries & Customer Problems.
• Develop New Product Capabilities.
• Build & Validate Proofs of Concept.

Skills

Python

Statistics

Machine Learning

Data Analysis

Structured and unstructured data

Model evaluation

Research skills

Communication skills

Presentation skills

Learning ability

Education

Bachelor's degree in Data Science, Computer Science, AI, Statistics, Mathematics, Engineering or related discipline

Tools

NumPy

Pandas

scikit-learn

Job description

Jora Malaysia will close on 9th September 2026. Thank you for being with us, we are cheering you on as you continue your career journey.

We are looking for a Data Scientist who is curious, analytical and commercially minded - someone who does not simply analyse data, but asks "What does this data tell us that the customer does not know today?" We value thinking, curiosity and problem-solving as much as technical skills.

You will work closely with our AI engineers, software developers, product team and industry specialists to discover new intelligence, develop analytical models and turn real-world data into practical capabilities for security and operations.

Responsibilities:

1. Discover Intelligence from Real-World Data

Analyse data from CCTV, computer vision, radar, sensors, access control, vehicles, workers, equipment and other sources.

Identify patterns, anomalies, correlations and relationships that may not be immediately visible.

Develop methods to determine what is normal, abnormal, risky or potentially important.

Explore how multiple data sources can be combined to create higher-value intelligence.

2. Develop AI & Data Science Models

Build and evaluate statistical, machine learning and AI models.

Develop models for anomaly detection, classification, prediction, risk scoring and behavioural analysis.

Analyse time-series, spatial, movement and event-based data.

Support computer vision models by developing the intelligence layer above raw detection.

Continuously evaluate model accuracy, false positives, false negatives and real-world performance.

Translate raw detections and model outputs into clear, decision-ready insights.

Frame analytical results in business terms: risk reduced, incidents prevented, efficiency gained

4. Research Industries & Customer Problems

Research industries that can benefit from Vision AI and operational intelligence.

Understand what information Project Directors, Security Managers, Operations Managers and Government agencies need to make better decisions.

Identify information gaps that existing CCTV and conventional systems cannot answer.

Benchmark competitors and emerging technologies globally.

Convert research findings into potential product capabilities and business opportunities.

5. Develop New Product Capabilities

Partner with product and engineering to define and prioritise new data-driven features.

Translate customer problems and research insights into concrete product requirements.

Help shape the roadmap for intelligence-led products and services.

6. Build & Validate Proofs of Concept

Rapidly prototype new analytical ideas.

Work with real customer/site data where available.

Test hypotheses and determine whether an idea is technically and commercially viable.

Present findings clearly to management, product teams and customers.

Translate complex analytical results into simple business insights.

Requirements:

Must-Have:

Degree in Data Science, Computer Science, AI, Statistics, Mathematics, Engineering or a related discipline.

Above 5 years related work experience in data science / advanced analytics.

Strong analytical and logical thinking.

Good understanding of statistics, machine learning and data analysis.

Experience with Python and common data science libraries.

Ability to work with structured and unstructured data.

Understanding of machine learning concepts and model evaluation.

Strong research and information-gathering ability.

Ability to independently learn unfamiliar technologies and industries.

Strong communication and presentation skills.