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Open nowPosted 401 days ago

Senior Data Scientist

rytbank92 open roles

Where
Kuala Lumpur
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Your applicationOpen nowSenior Data Scientistrytbank · Kuala Lumpur

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on rytbank's own form.

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The clock on this job

Early applications get read.

13.8% of postings close within 7 days. Measured by our own scanner across the market. rytbank postings stay open a median of 19 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.8%7 days
  4. 22.3%14 days
  5. 37.8%30 days
  6. 59.0%60 days
This job: posted 401 days ago

rytbank median: 19 days open

The posting

Position Overview

We are seeking an exceptional Senior Data Scientist to drive product innovation through advanced analytics, experimentation, and machine learning. Embedded within cross-functional product teams, you will deliver measurable business impact by building and deploying solutions across fraud detection, credit decisioning, and user experience optimisation. You will own the full lifecycle – from designing experiments and developing models to production deployment and performance monitoring. This role demands both technical excellence and strategic thinking to identify high-impact opportunities and deliver data-driven results in a fast-paced fintech environment.

Key Responsibilities

Strategic Analytics & Product Innovation

  • Design and implement advanced machine learning models for credit risk assessment, fraud detection, customer lifetime value prediction, and personalized product recommendations
  • Partner with product teams to translate business challenges into data science solutions that drive measurable impact
  • Build predictive models for customer churn, acquisition optimisation, and engagement strategies
  • Develop scoring systems for loan decisioning and transaction monitoring

Technical Leadership

  • Lead end-to-end model development lifecycle from experimentation to production deployment
  • Partner with engineering teams to design and deploy scalable ML pipelines using modern cloud infrastructure
  • Establish best practices for model governance, monitoring, and validation
  • Mentor junior data scientists and promote a culture of analytical excellence

Business Impact

  • Collaborate with stakeholders across Risk, Marketing, Product, and Engineering to identify opportunities for data-driven optimisation
  • Communicate complex technical concepts and insights to non-technical executives through compelling data storytelling
  • Design and analyze A/B tests to measure feature impact and guide product decisions

Required Qualifications

Technical Expertise

  • 5+ years of experience in data science, with at least 2 years in fintech, banking, or financial services
  • Expert-level proficiency in Python and ML frameworks (Tensorflow, PyTorch, XGBoost)
  • Strong foundation in statistical methods, hypothesis testing, and experimental design
  • Production experience with SQL and working with large-scale datasets (terabytes+)
  • Proven track record deploying and maintaining ML model pipelines in production environments

Domain Knowledge

  • Deep understanding of financial services concepts including credit risk, fraud patterns, regulatory compliance (KYC / AML), and customer behaviour
  • Experience with time-series forecasting, anomaly detection, and classification problems
  • Familiarity with ML model explainability techniques (SHAP, LIME) and responsible AI practices
  • Knowledge of model governance frameworks

Business & Leadership Skills

  • Outstanding problem-solving abilities with a product-minded approach
  • Excellent communication skills with ability to influence key stakeholders
  • Collaborative team player who thrives in fast-paced, agile environments
  • Strong business acumen and ability to balance technical rigour with practical constraints

Preferred Qualifications

  • Experience with real-time ML systems and stream processing (Kafka, Spark Streaming)
  • Knowledge of LLMs and generative AI applications in banking
  • Experience with MLOps tools (MLflow, Kubeflow, SageMaker)
  • Prior experience building recommendation engines or personalisation systems

Growth Opportunities

  • Shape the analytics culture and best practices across the organization
  • Opportunity to influence product strategy through data-driven insights
  • Career progression toward senior leadership roles in data and analytics

JR00000443

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