Pay
$118K–$193K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,721 postings in the last year. Stated ranges, not salaries paid.
თბილისი, Georgia
Live, first seen by us 18 days ago0–1 year of relevant working experience
Validate models, assessing performance, data quality and methodology
Run model outcome analysis, including back-testing
Build benchmark and challenger models to assess models under review
Write validation reports and present findings to the immediate team
Monitor model performance to spot model risks early
Conduct model validations and assess model performance, conceptual soundness, input data quality, and development methodology.
Carry out model outcomes analysis, including back-testing exercises.
Develop benchmark and challenger models to support the assessment of models under review.
Prepare clear, well-structured validation reports and present findings and observations to the immediate team.
Monitor model performance to support early and proactive identification of model risks.
Degree in Statistics, Mathematics, Data Science, Computer Science, or a related field.
0–1 year of relevant working experience; a strong academic background, internship experience, or a portfolio of personal and academic projects is acceptable in lieu of professional experience.
Solid foundational knowledge of statistical modeling and machine learning concepts.
Proficiency in Python and/or R; working knowledge of SQL for data manipulation and analysis.
Proficiency in writing clearly and concisely in both Georgian and English.
Experience in the financial sector, particularly banking, is an advantage but is not required.
Familiarity with deep learning, reinforcement learning, or natural language processing is an advantage.
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$118K–$193K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,721 postings in the last year. Stated ranges, not salaries paid.
Day 18
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From 9,988 similar postings that came down. Coming down is not always a hire.
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- Awareness of Retrieval-Augmented Generation (RAG) systems and their components, including vector databases, embedding models, and document retrieval pipelines, is a plus. - Proficiency in Python and/or R; working knowledge of SQL for data manipulation and analysis. - Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.) and Big Data technologies (Hadoop/Hive, Spark, NoSQL, etc.) is a plus.
- Proficiency in Python and/or R; working knowledge of SQL for data manipulation and analysis. - Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.) and Big Data technologies (Hadoop/Hive, Spark, NoSQL, etc.) is a plus. - Knowledge of data visualization libraries (such as Matplotlib or Plotly) and tools (such as Microsoft Power BI or Tableau) is a plus.
- Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.) and Big Data technologies (Hadoop/Hive, Spark, NoSQL, etc.) is a plus. - Knowledge of data visualization libraries (such as Matplotlib or Plotly) and tools (such as Microsoft Power BI or Tableau) is a plus. Knowledge, Skills and Abilities:
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We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
Model Risk Management Department at Bank of Georgia is looking for passionate, curious, and talented Junior Data Scientist to join our growing team. You will have an opportunity to contribute to various innovative data-driven products and scalable intelligent systems. Members of the team are testing, validating, challenging and monitoring all the AI/ML models and solutions developed/used in the bank in order to mitigate model risk and create safe environment for operations in different areas of business (credit risk, marketing, customer care, optimization of back office operations, etc.). We are looking for people interested to explore business insights, with foundational knowledge in model development and validation eager to learn how to identify, assess and mitigate model risks. If you are a resourceful, goal-oriented person enjoying collaboration to deliver solutions, this may be an opportunity for you
Key Responsibilities:
All responsibilities below are carried out within a structured team environment, with guidance and oversight from senior team members.
Conduct model validations and assess model performance, conceptual soundness, input data quality, and development methodology.
Carry out model outcomes analysis, including back-testing exercises.
Evaluate new and existing models' overall fit and suitability with their intended use and purpose.
Identify models' key assumptions, limitations, and weaknesses, assess the effectiveness of compensating controls, and contribute to practical risk mitigation recommendations.
Develop benchmark and challenger models to support the assessment of models under review.
Prepare clear, well-structured validation reports and present findings and observations to the immediate team.
Develop familiarity with the Model Risk Policy and apply relevant requirements in day-to-day work.
Monitor model performance to support early and proactive identification of model risks.
Support model governance activities and contribute to the optimization of the validation and monitoring process through automation, incorporating regulatory changes and other initiatives.
Preferred Qualifications:
Degree in Statistics, Mathematics, Data Science, Computer Science, or a related field.
0–1 year of relevant working experience; a strong academic background, internship experience, or a portfolio of personal and academic projects is acceptable in lieu of professional experience.
Experience in the financial sector, particularly banking, is an advantage but is not required.
Exposure to working with structured datasets; experience with complex or large-scale data is a plus.
Solid foundational knowledge of statistical modeling and machine learning concepts.
Familiarity with deep learning, reinforcement learning, or natural language processing is an advantage.
Familiarity with Large Language Models (LLMs) and their practical applications; exposure to prompt engineering, fine-tuning, or LLM evaluation is a plus.
Basic understanding of transformer architecture and attention mechanisms; hands-on experience with transformer-based models through academic or personal projects is an advantage.
Awareness of Retrieval-Augmented Generation (RAG) systems and their components, including vector databases, embedding models, and document retrieval pipelines, is a plus.
Proficiency in Python and/or R; working knowledge of SQL for data manipulation and analysis.
Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.) and Big Data technologies (Hadoop/Hive, Spark, NoSQL, etc.) is a plus.
Knowledge of data visualization libraries (such as Matplotlib or Plotly) and tools (such as Microsoft Power BI or Tableau) is a plus.
Knowledge, Skills and Abilities:
Knowledge of relational database concepts and data modeling.
Foundational data visualization and storytelling skills; ability to build basic dashboards using visualization tools, with guidance.
Ability to communicate analytical insights using data to both technical and non-technical audiences.
Strong analytical and problem-solving skills.
Critical thinking and attention to detail.
Team player and self-starter, with a desire to learn and add value, and a can-do attitude.
Ability to troubleshoot issues methodically and escalate to senior colleagues where appropriate.
Receptiveness to feedback and mentorship as part of ongoing professional development.
Proficiency in writing clearly and concisely in both Georgian and English.
Interested candidates, please fill in the information, attach your CV and submit by clicking “apply for position now”. Deadline is October 30 2026.
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თბილისი, Georgia
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