Data Science Associate

Who We Are:

At Kayana, we're shaping the future of technology by unlocking opportunities and driving progress at an accelerated pace. We push boundaries, expand horizons, and build on our legacy—supported by some of the country's most esteemed conglomerates and powered by the synergies of the MVP Group.

Our mission is clear: to create abundant opportunities and pave the way for generational progress for Filipinos. Harnessing the power of data and technology, we are leading the charge in crafting a new world where individuals and businesses are seamlessly connected to a digital universe, enabling them to do more and be more.

We're turning the impossible into possible. Sa Káyana, kaya na.

Job Summary:

The Data Science Associate will design, develop, and deploy machine learning models, generate actionable insights, and integrate external datasets (e.g., PSA, DOH, DOE) to strengthen analytics capabilities. This role is central to advancing Kayana's MLOps practices, ensuring models are scalable, reliable, and ethically applied.

The role also involves exploring emerging technologies such as Generative AI and LLMOps, building prototypes, and contributing to innovation initiatives. The associate may also mentor interns under Kayana's academic partnership programs.

Key Responsibilities:

• Model Development & Deployment - Build, train, and deploy predictive and generative ML models into production. Monitor, retrain, and optimize models to ensure fairness, scalability, and performance.

• Analytics & Insights – Extract and communicate insights from internal, public, and third-party datasets to support decision-making and product development.

• Data Enrichment – Acquire, integrate, and validate external data sources to improve model accuracy and analytical depth.

• MLOps & Automation – Implement best practices for versioning, monitoring, and retraining models. Contribute to strengthening Kayana's MLOps ecosystem.

• Innovation & Research – Explore and evaluate emerging tools, frameworks, and techniques in AI/ML, Generative AI, and MLOps. Deliver prototypes and proof-of-concepts for testing and adoption.

• Collaboration & Mentorship – Work closely with data engineers, product managers, and domain experts to deliver scalable solutions. Mentor interns and junior associates to build future talent pipelines.

Qualifications:

• Proven experience in developing and deploying ML models (e.g., classification, regression, clustering, forecasting)

• Strong programming skills in Python, with expertise in ML libraries such as scikit-learn, PyTorch, TensorFlow, Hugging Face.

• Familiarity with MLOps tools and practices (MLflow, Feast, CI/CD, model monitoring).

• Solid foundation in data engineering concepts, SQL, and cloud platforms (AWS, Databricks, Spark preferred).

• Experience integrating public or third-party datasets into analytical workflows.

• Understanding of ethical AI, model explainability, and bias detection.

• Strong analytical thinking, problem-solving, and communication skills.

Education:

• Bachelor's degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a related field. Master's degree or equivalent experience in data science, machine learning, or AI is an advantage.

Preferred Skills:

• Strong programming in Python, PySpark, and SQL, with hands-on expertise in Databricks.

• Proficiency with BI tools such as Power BI, Metabase, or Superset for visualization and reporting.

• Experience in data warehouse modeling and analytics engineering practices.

• Knowledge of data lake architectures for large-scale data management.

• Proficiency with ML frameworks such as scikit-learn, LightGBM, XGBoost, CatBoost, TensorFlow, PyTorch, Hugging Face.

• Exposure to Generative AI frameworks (LangChain, LlamaIndex, vector databases)

• Hands-on experience with MLOps practices and tools (MLflow, Feast, CI/CD pipelines, monitoring)

• Familiarity with DataOps/orchestration tools (Airflow, Prefect, Dagster)

• Understanding of Medallion architecture (Bronze/Silver/Gold layers) and Feature Stores.

• Strong data storytelling, dashboarding, and visualization capabilities.

• Understanding of cloud platforms, especially AWS (SageMaker, S3, Redshift), will be an advantage.

• Understanding of big data computing (Spark clusters, distributed systems) and GPU acceleration for deep learning and Generative AI.