Data Scientist
Krungthai Bank PCL.
We are looking for a Data Scientist to help the bank make smarter, data-driven decisions across credit, risk, fraud, and customer analytics. You will work with large-scale financial and customer data to build, deploy, and monitor models that are accurate, explainable, and compliant with banking regulations.
You will collaborate closely with business, risk, and technology teams to turn data into insights, models into products, and analytics into measurable business impact.
Data Scientist is a role that combines elements of computer science, statistics, and domain expertise in order to extract insights and knowledge from data. The Data Scientist is responsible for analyzing data sets, processing, cleansing, and verifying the integrity of data used for analysis, supporting with preparation of datasets for advanced analytics, researching, developing, and implementing prediction, optimization, and analytics tools and documenting and communicating methodology and results to technical and non-technical audiences. This role requires a combination of technical, statistical, and business skills to extract insights from data and drive business decision- making.
Responsibilities
Directing the data gathering, data mining, and data processing processes in huge volume discover patterns and appropriate data models to discover the values of the data to meet the organization's unique needs.
Leading to define requirements and scope of data analyses; presenting and reporting possible business insights to management using data visualization technologies.
Utilize machine learning to build predictive or prescriptive models in order to extract insights and drive business decision-making by working closely with AI Engineers.
Conducting research on data model optimization and algorithms to improve effectiveness and accuracy on data analyses.
Design and analyses metrics to verify model and algorithm effectiveness.
1. Predictive & Prescriptive Modeling
- Develop, validate, and maintain predictive models using statistical and machine learning techniques (e.g., regression, classification, clustering, time-series forecasting).
- Build models for use cases such as credit scoring, fraud detection, customer churn prediction, customer lifetime value, and cross-/upsell.
- Perform model performance evaluation (e.g., ROC/AUC, KS, lift, precision/recall) and ensure robustness across segments and time.
2. GenAI, NLP & Unstructured Data
- Design and implement GenAI / LLM and NLP solutions to support use cases such as document understanding (e.g., KYC documents, financial statements), customer interaction analytics, or knowledge search.
- Work with unstructured data (text, documents, logs) using deep learning frameworks where appropriate.
3. Data Preparation & Feature Engineering
- Explore and transform large volumes of transactional, behavioral, and external data into high-quality features.
- Apply strong domain understanding of banking products (loan, card, deposit, digital channels) to design meaningful features that improve model performance.
- Ensure data quality and consistency through profiling and collaboration with data engineering teams.
4. Experimentation & Business Impact Measurement
- Design and analyze A/B tests and controlled experiments to measure the impact of models and product changes on business KPIs (revenue, risk, engagement, cost).
- Translate analytical findings into clear recommendations and decision options for stakeholders.
5. Model Explain ability, Risk & Governance
- Ensure models are transparent, explainable, and auditable using tools such as SHAP / LIME and appropriate documentation.
- Work with Risk, Compliance, and Model Risk Management (MRM) teams to meet regulatory and internal policy requirements (e.g., fairness, bias detection, stability, documentation).
- Contribute to model governance processes including model inventory, periodic review, and re-calibration.
6. Deployment, Monitoring & Collaboration
- Partner with data engineers / MLOps engineers to productionize models (batch or real-time) on the bank's data / AI platform.
- Set up monitoring dashboards and alerts for model performance, data drift, and business KPIs.
- Collaborate with product owners, business users, IT, and operations to ensure successful adoption of analytics solutions.
Qualifications
Education
- Master's degree (or higher) in Data Science, Statistics, Mathematics, Computer Science, Engineering, or related quantitative field.
Experience
- 3+ years of hands-on experience in data science, statistical modeling, or machine learning (experience in Banking / FinTech / Insurance is a strong plus).
Technical Skill Must Have
- Strong programming skills in Python (e.g., Pandas, NumPy, Scikit-learn) and SQL for working with large datasets.
- Proven experience building and validating models using tree-based methods (e.g., XGBoost, LightGBM, CatBoost) or similar algorithms.
- Solid understanding of statistics and machine learning concepts (sampling, hypothesis testing, regularization, cross-validation).
- Experience in presenting analytical results to non-technical stakeholders in a clear and structured way.
- Good command of English; ability to work with regional/global teams is an advantage.
Technical Skill - Nice-to-Have
- Experience with deep learning frameworks (TensorFlow, PyTorch) for text or other unstructured data.
- Familiarity with GenAI / LLM frameworks (e.g., LangChain, Hugging Face) and prompt engineering concepts.
- Experience with data visualization and dashboarding tools (Tableau, Power BI, or similar).
- Experience working on MLOps / model deployment pipelines (e.g., Docker, APIs, CI/CD, MLflow).
- Prior experience in credit risk, fraud analytics, marketing analytics, or customer analytics in a financial institution.
Key Competencies & Mindset
- Business Acumen: Able to understand banking products and connect models to revenue, risk, and customer experience.
- Problem-Solving: Structured thinking, able to frame business questions into analytical problems and propose practical solutions.
- Communication & Storytelling: Capable of simplifying complex analysis into clear messages, visuals, and recommendations.
- Curiosity & Learning Agility: Strong desire to explore data, ask why, and continuously learn new tools and methods.
- Collaboration: Comfortable working in cross-functional teams with business, risk, technology, and operations stakeholders.
Contact : 062-954-1963 (K.Kanyarut)
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