Senior Data Scientist
At a glance
Senior Data Science role at Kroll. Manila · full-time.
Pay not stated Data Scientist salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and assess machine learning models, from statistical methods and deep learning to NLP and generative AI
- Create scalable data workflows in Databricks, then deploy and maintain models on Azure
- Set up LLM prompts, quality checks and safeguards, including retrieval-augmented generation systems
- Work with business, product and engineering partners to turn problems into measurable ML outcomes
- Explain model results to technical and non-technical audiences, guide junior scientists and strengthen team practices
What you bring
- An MS or PhD in computer science, statistics, mathematics, analytics or another related quantitative field
- At least five years applying machine learning, including taking models into production
- Strong Python skills and experience with tools such as scikit-learn, PyTorch or TensorFlow, pandas and Hugging Face Transformers
- Practical Databricks and Spark experience, plus production work on Azure
- Experience across the ML lifecycle, including generative AI methods, data checks, deployment and monitoring
Who this fits
This senior practitioner role suits someone ready to lead machine learning work from initial design through production and mentor junior colleagues. You’ll collaborate with engineering, product and business teams and communicate findings to senior leaders and clients. The role is based in Manila and is hybrid.
From the employer
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. Apply now to join 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 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
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