Senior Data Scientist

Kroll Global Solutions Inc.; Manila; On-site; PHP 1,800,000 - 3,000,000; Full time

Job summary

Kroll is hiring a Senior Data Scientist to join its Enterprise Data Group in Manila. You will lead end-to-end ML initiatives, mentor junior team members, and partner with business and engineering stakeholders to translate complex problems into production-grade data science solutions.

The role spans fintech product development, digital transformation, process automation with ML, BI, data governance, and generative AI, with collaboration across world-class financial institutions and government

Qualifications

• Advanced degree (MS or PhD) in CS, statistics, mathematics, analytics, or related field.
• 5+ years of applied machine learning experience, including delivering models to production.
• Strong Python skills and the modern ML stack (scikit-learn, PyTorch or TensorFlow, pandas, Hugging Face Transformers).
• Hands-on experience with Databricks and Spark/PySpark.
• Production experience on Azure — including Azure AI Foundry, Azure OpenAI Service, and Azure Data Lake.
• Breadth across ML domains: traditional ML, deep learning, NLP, and GenAI; prompt engineering, RAG, embeddings, agentic workflows.
• Experience building LLM/GenAI applications — prompt engineering, RAG, fine-tuning, embeddings, vector databases, evaluation.
• Full ML lifecycle: data validation, feature engineering, model design, experimentation, deployment, monitoring.
• Experience with structured and unstructured data, including text, documents, and semi-structured sources.
• Strong statistical foundation and ability to reason about uncertainty, bias, and model risk.
• Excellent technical and business communication skills.

Responsibilities

• Design, research, implement, and evaluate ML solutions spanning traditional ML, deep learning, NLP, and LLM/GenAI applications
• Build and fine-tune models — from gradient-boosted trees and transformer-based architectures and RAG systems
• Develop and optimize prompts, evaluation frameworks, and guardrails for LLM-powered applications
• Engineer scalable data and ML pipelines in Databricks using PySpark, Delta Lake, and MLflow
• Deploy, monitor, and maintain models in production on Azure (Azure AI Foundry, Azure OpenAI, Azure Functions, AKS), including CI/CD, model versioning, and drift detection
• Validate model inputs, outputs, and business impact; establish robust testing and monitoring practices
• Partner with engineering, product, and business stakeholders to scope problems and translate ML capabilities into measurable outcomes
• Communicate technical concepts, tradeoffs, and results to non-technical audiences, including senior leadership and clients
• Mentor junior data scientists and contribute to team standards around code quality, experimentation, and responsible AI

Skills

Python programming

ML model deployment

Azure ML

Databricks

Spark/PySpark

Hugging Face Transformers

LangChain

Education

MS or PhD in CS/Statistics/Math/Analytics

Tools

Databricks

MLflow

Azure OpenAI Service

Azure AI Foundry

Unity Catalog

Spark/PySpark

Azure Data Lake

Job description

Kroll is hiring a Senior Data Scientist to join its Enterprise Data Group. This role is designed for an experienced practitioner who can lead end-to-end ML initiatives, mentor junior team members, and partner with business and engineering stakeholders to translate complex problems into production-grade data science solutions. Our program spans fintech product development, digital transformation, process automation with machine learning, business intelligence, data governance, and generative AI.

You will work alongside an advanced data science and engineering team — and collaborate with professionals from the world's largest financial institutions, law enforcement agencies, and government bodies. At Kroll, your work will help deliver clarity to our clients' most complex governance, risk, and transparency challenges.

One team, One Kroll.

Responsibilities

• Design, research, implement, and evaluate machine learning solutions spanning traditional ML, deep learning, NLP, and LLM/GenAI applications
• Build and fine-tune models — from gradient-boosted trees and classical statistical models to transformer-based architectures and retrieval-augmented generation (RAG) systems
• Develop and optimize prompts, evaluation frameworks, and guardrails for LLM-powered applications
• Engineer scalable data and ML pipelines in Databricks using PySpark, Delta Lake, and MLflow
• Deploy, monitor, and maintain models in production on Azure (Azure AI Foundry, Azure OpenAI, Azure Functions, AKS), including CI/CD, model versioning, and drift detection
• Validate model inputs, outputs, and business impact; establish robust testing and monitoring practices
• Partner with engineering, product, and business stakeholders to scope problems and translate ML capabilities into measurable outcomes
• Communicate technical concepts, tradeoffs, and results to non-technical audiences, including senior leadership and clients
• Mentor junior data scientists and contribute to team standards around code quality, experimentation, and responsible AI

Requirements

• Advanced degree (MS or PhD) in computer science, statistics, mathematics, analytics, or a related quantitative field
• 5+ years of applied machine learning experience, including delivering models to production
• Strong Python skills and experience with the modern ML stack (scikit-learn, PyTorch or TensorFlow, pandas, Hugging Face Transformers)
• Hands-on experience with Databricks (notebooks, jobs, MLflow, Unity Catalog) and Spark/PySpark
• Production experience on Azure — ideally including Azure AI Foundry, Azure OpenAI Service, and Azure Data Lake
• Breadth across ML domains: traditional/statistical ML, deep learning, NLP, and LLM/GenAI applications, including hands-on experience with prompt engineering, RAG, embeddings, and agentic workflows
• Practical experience building LLM/GenAI applications — prompt engineering, RAG, fine-tuning, embeddings, vector databases, and evaluation
• Solid grounding in the full ML lifecycle: data validation, feature engineering, model design, experimentation, deployment, and monitoring
• Experience with structured and unstructured data, including text, documents, and semi-structured sources
• Strong statistical foundation and ability to reason about uncertainty, bias, and model risk
• Excellent technical and business communication skills

Preferred Experience

• Preferred Experience in financial services, risk, compliance, or regulatory domains
• Familiarity with MLOps tooling (MLflow, Docker, Kubernetes, Azure DevOps or GitHub Actions)
• Hands-on experience with agentic AI frameworks (LangChain, LlamaIndex, Semantic Kernel), LLM evaluation tooling, and production deployment of GenAI applications
• Knowledge of responsible AI practices, including fairness, explainability, and data privacy