Data Scientist

Who We Are

We are trusted, modern technology leaders in:

• Agile Software Development
• Quality Assurance
• Cloud Consulting and Managed Services
• Data Engineering and Analytics
• Artificial Intelligence and Machine Learning

We use technology to enrich experiences, empower businesses, and uplift communities.

Our C.A.R.E. values:

• nourish Creativity
• embody Agility
• manifest Reliability
• pursue Evolution

The Role

The Data Scientist plays a key role in supporting the Data Team’s vision of building a data ecosystem that delivers accurate, timely, and well-structured data to enable the cycle of data asset enrichment for broader enterprise impact. This role is both technical and strategic — contributing to the delivery of high-quality data and model solutions while also helping build data foundations through active involvement in data governance initiatives.

The Data Scientist will collaborate closely with both internal stakeholders (Product Leads, Machine Learning Engineers, and Software Developers) as well as external stakeholders (inc. other business units) as they enable use cases, advance operational efficiency and effectiveness, and champion data-driven culture.

The Benefits

At Stratpoint, we enjoy the autonomy, lack of bureaucracy, and the freedom to experiment but without the chaos. We’re like a startup but with adult supervision.

You will be supported by capable management of HR, Finance and IT that adheres to the highest standards of integrity and good governance.

Hybrid is our mode of work, but when we need to come together, we have inviting open floor office spaces designed to spark gatherings, collaboration, and camaraderie.

Stratpoint employees enjoy:

• SSS, Pag-ibig and Philhealth benefits + company contribution
• a monthly miscellaneous allowance
• 13th month pay based on your monthly rate.
• Vacation and Sick leave
• Wellness programs such as Wellness Wednesdays
• HMO coverage
• Team buildings and quarterly events (company outings and festive celebrations for Chinese New Year, Valentines, Halloween and Christmas)
• And, friendly intramural competitions

Your Team

You will be working with a team of AI Engineers, Data Scientists and Software Engineers of different tech stacks depending on the project you will be assigned to under Labs.

• Develop and implement forecasting models, including time series and hierarchical forecasting frameworks (e.g., bottom-up, top-down, reconciliation methods)
• Apply statistical, machine learning, and deep learning techniques to solve real-world prediction problems
• Design and maintain automated data pipelines, including data ingestion, preprocessing, model training, and output generation
• Work with sparse, noisy, or incomplete datasets, ensuring robustness and reliability of models
• Perform data analysis and visualization, including the creation of clear and insightful forecasting graphs and dashboards
• Integrate data from open-source datasets and external data vendors
• Conduct basic web scraping and/or develop synthetic data generation pipelines where data is limited
• Collaborate with cross-functional teams to translate business requirements into scalable AI solutions

• Bachelor’s or Master’s degree in Data Science, Statistics, Physics, Math, Computer Science, Engineering, or a related quantitative field
• Proven experience in time series forecasting and statistical modeling
• Solid understanding of: Machine Learning (e.g., regression, tree-based models, ensemble methods); Deep Learning (e.g., RNNs, LSTMs, Transformers for time series); Statistical methods (e.g., inference, probabilistic modeling)
• Experience working with hierarchical or multi-level datasets
• Proficiency in Python, common data science libraries and tools such as: NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow, Nixtla; Data visualization frameworks (Matplotlib, Seaborn, Plotly); Fast API, Streamlit, Gradio; Docker, Git; AWS (S3, RDS, Redshift); SQL; Marimo (or similar reproducible notebooks); Databricks or similar platforms
• Experience building end-to-end data workflows, including ingestion, transformation, modeling, and output
• Strong skills in data visualization and graphical analysis
• Effective communication skills, and able to clearly document work (pipelines, flowcharts, ERDs, BRDs)

Nice to have:

• Experience in healthcare or financial data domains, including cost optimization use cases
• Familiarity with sparse data modeling techniques and uncertainty quantification
• Experience with data engineering concepts (ETL pipelines, data warehousing), and can integrate open-source and external data
• Exposure to cloud platforms (e.g., AWS, GCP, Azure)
• Knowledge of MLOps practices (model deployment, monitoring, CI/CD for ML systems)
• Experience with web scraping tools (e.g., BeautifulSoup, Scrapy) or synthetic data generation techniques
• Familiarity with data governance, including frameworks for data quality checks, lineage, security and compliance
• Familiarity with experiment tracking and model versioning tools
• Experience with distributed data systems and scalable computing
• Understanding of optimization techniques and numerical methods

We look forward to getting to know you more!