Senior Data Scientist - Hybrid

Location: Taguig, Philippines

Work Setup: Hybrid, 3 Days WFH and 2 Days Onsite

Compensation Flexibility: Open to negotiation for highly qualified candidates.

KEY RESPONSIBILITIES

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  Research, design, develop, and deploy machine learning models and statistical algorithms to analyze, optimize, and improve the reliability of subsea fiber optic infrastructure.

  
  


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  Lead R&D initiatives involving predictive analytics, anomaly detection, subsea route optimization, risk modeling, and infrastructure reliability.

  
  


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  Conduct advanced geospatial analyses to support subsea cable route planning, monitoring, and optimization using spatial data frameworks and mapping tools.

  
  


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  Develop geospatial data models, analytical workflows, dashboards, and visualizations to communicate geographic, environmental, and infrastructure insights to technical and business stakeholders.

  
  


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  Design, build, and maintain scalable data pipelines and ETL/ELT workflows for ingesting, transforming, validating, and processing large-scale structured and unstructured datasets.

  
  


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  Integrate geospatial intelligence capabilities into machine learning models and analytical pipelines while ensuring data quality, integrity, and processing efficiency.

  
  


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  Leverage Large Language Models (LLMs) and generative AI to automate research, data extraction, knowledge discovery, analysis, and internal operational workflows.

  
  


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  Develop intelligent systems combining traditional machine learning with LLM-based capabilities, including prompt engineering, retrieval-augmented generation (RAG), and AI-assisted knowledge extraction.

  
  


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  Build and maintain internal tools, web applications, REST APIs, command-line utilities, and automation scripts to improve engineering and R&D processes.

  
  


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  Develop lightweight analytical applications using frameworks such as Streamlit, FastAPI, or Flask, following software engineering best practices for scalability, maintainability, and reusability.

  
  


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  Perform experimentation, model validation, benchmarking, testing, debugging, and performance optimization to ensure production readiness.

  
  


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  Collaborate with data scientists, software engineers, infrastructure specialists, and business stakeholders on technical strategy, solution architecture, and R&D initiatives.

  
  


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  Document technical designs, analytical methodologies, workflows, and implementation details, and communicate findings to technical and non-technical audiences.

  
  

REQUIRED QUALIFICATIONS

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  3–5 years of experience designing and deploying advanced algorithms, machine learning models, statistical techniques, and data pipelines for analytical and optimization use cases.

  
  


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  Strong experience developing and deploying machine learning models and statistical frameworks in production environments.

  
  


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  Advanced proficiency in Python and its data science ecosystem, including NumPy, Pandas, and Scikit-learn, with experience using PyTorch or TensorFlow.

  
  


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  Hands-on experience with geospatial analysis, spatial data processing, and mapping tools such as GeoPandas, Google Earth Engine (GEE), and QGIS.

  
  


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  Experience architecting and maintaining scalable data pipelines for large-scale datasets, including data ingestion, transformation, validation, and processing.

  
  


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  Experience developing functional web applications using Streamlit, FastAPI, Flask, or similar frameworks.

  
  


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  Ability to develop REST APIs, command-line interface (CLI) utilities, and automation scripts for analytical, engineering, and research workflows.

  
  


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  Experience leveraging LLMs or generative AI technologies to automate processes, extract information, and generate insights from diverse data sources.

  
  


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  Strong analytical, statistical, and problem-solving skills, with the ability to evaluate model performance and translate complex data into actionable insights.

  
  


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  Strong communication and collaboration skills, with the ability to work across multidisciplinary technical teams and communicate findings to varied stakeholders.

  
  

PREFERRED QUALIFICATIONS

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  Direct experience working with subsea fiber optic infrastructure, submarine cable networks, telecommunications infrastructure, or related engineering domains.

  
  


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  Experience applying geospatial intelligence and machine learning to subsea route planning, infrastructure monitoring, environmental analysis, risk assessment, or reliability optimization.

  
  


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  Familiarity with retrieval-augmented generation (RAG), prompt engineering, and AI-assisted knowledge extraction.

  
  


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  Experience integrating machine learning models, geospatial platforms, and LLM-based capabilities into production systems.

  
  


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  Familiarity with software testing, code reviews, deployment practices, technical documentation, and scalable application architecture.

  
  

REQUIRED SKILLS & TECHNOLOGIES

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  Programming: Python.

  
  


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  Data Science & Machine Learning: NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow, statistical modeling, predictive analytics, anomaly detection, and model validation.

  
  


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  Geospatial Analytics: GeoPandas, Google Earth Engine (GEE), QGIS, spatial data modeling, mapping workflows, and geospatial visualization.

  
  


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  Data Engineering: Data pipelines, ETL/ELT, large-scale dataset processing, data validation, data quality management, and storage optimization.

  
  


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  AI & LLMs: Large Language Models, generative AI, prompt engineering, retrieval-augmented generation (RAG), and AI-assisted data extraction.

  
  


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  Application Development: Streamlit, FastAPI, Flask, REST APIs, web applications, CLI utilities, and automation scripts.

  
  


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  Infrastructure Analytics: Subsea route optimization, infrastructure monitoring, risk modeling, predictive analytics, and reliability analysis.

  
  


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  Software Engineering: Testing, debugging, code reviews, documentation, performance optimization, and maintainable software development.

  
  


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  Core Competencies: Research and development, statistical analysis, critical thinking, technical communication, cross-functional collaboration, and solution design.