Level

RAKBANK

Staff Data Scientist

RAKBANK is hiring a Staff Data Scientist. Level rates it ; you can apply on Level.

AI in this role

nlp

The Staff Data Science will lead the development of AI/ML solutions that drive data‑driven decision‑making across customer engagement, marketing, retention, risk, and product functions. The role requires strong analytical expertise, hands‑on experience with open‑source technologies, and the ability to build interactive applications and deploy models in both batch and real‑time environments. The candidate will ensure high‑quality delivery, effective stakeholder communication, and adherence to best practices for reproducible, scalable data science.

What You Will Do:

  • Work with Data Science team to translate given business problem into analytical use-cases with defined outcomes to develop, implement and test most appropriate algorithms for a given use-case.
  • Work closely with Business Analyst for requirement gathering/understanding and work with Data Engineers to build data pipelines and automate/production Alize complex ML models to insights and recommendations.
  • Strong conceptual understanding of machine learning algorithms including multi-variate regressions, classification algorithms, time series techniques, clustering, NLP, Image Processing, and optimization models etc.
  • Ensure high coding standards as well as designing standards to ensure reproducibility.
    Drive innovation by enhancing existing solutions and designing new ones and build collaboration and awareness in the bank’s analytics community 
  • Work closely with various business units/stakeholders to identify and streamline the AI-ML use-cases.
  • Deliver end-to-end AI-ML models from development to deployment with delivery planning, communications with stakeholders, and ensuring efficient usage of the models by the business as recommendations for improving decision touchpoints.
  • Ensure high coding standards, peer-review, and transparency in the work with the line of reporting.
  • Support ad-hoc requirement in terms of MIS development, building and analyzing SAS Data   Models, streamlining the reporting process through various reporting and analytical tools.

What We Are Looking For:

  • Overall 7+ years of experience in analytics, data science or similar function
  • 3-4 years of experience in banking analytics
  • Python and SQL experience required
  • Worked on end-to-end ML model deployment
  • Machine Learning / Deep Learning / Time-Series / Optimization and Customer Analytics

Technical Skills:

  • Strong coding skills using Python. 
  • Knowledge on Retail Banking Products
  • Deployment experience (various databases, server/cloud environment: AWS, Azure, and APIs, ODBCs, web apps) 
  • Excellent knowledge of Banking Functional Knowledge (major plus) 
  • Good written, oral communication, documentation skills with ability to communicate effectively with stakeholders.
  • Expert-level proficiency in Python with SAS/R/Spark as plus 

How we rate this

Staff Data Scientist at RAKBANK rates 86 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

NLP

Questions you could be asked

  1. What NLP problem have you worked on, and how did you measure whether it actually worked?
  2. How would you decide a model or AI system is ready to ship?
  3. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: NLP. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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