Senior Data Scientist - Hybrid

Lennor Group; Taguig; Hybrid; PHP 600,000 - 1,200,000; Full time

Job summary

Lennor Group's client in Taguig, Philippines is seeking a skilled ML engineer to research, design, and deploy machine learning models and statistical algorithms to analyze and improve the reliability of subsea fiber optic infrastructure.

You will lead R&D initiatives involving predictive analytics, anomaly detection, subsea route optimization, and risk modeling, collaborating with cross-functional teams to deliver robust solutions in a hybrid work environment (3 days WFH, 2 days onsite).

Qualifications

• Experience building ML models for real-world data.
• Strong analytical and problem-solving skills.
• Ability to collaborate with cross-functional teams.

Responsibilities

• Research, design, and deploy ML models for subsea fiber optic infrastructure.
• Lead R&D initiatives involving predictive analytics, anomaly detection, and route optimization.

Skills

Machine Learning

Python

Data Analysis

Tools

TensorFlow

PyTorch

Job description

Company Overview

Our client is an organization operating within the

Our client is an organization operating within the BPO industry, focused on providing tech solutions for its clients. The company serves North American, European, and APAC regions and is committed to delivering quality solutions, operational excellence, and sustainable business growth. Additional information about the organization will be shared with selected candidates at the appropriate stage of the recruitment process.

Location: Taguig, Philippines

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

Compensation Flexibility: Open to negotiation for highly qualified candidates.

Key Responsibilities

• Research, design, develop, and deploy machine learning models and statistical algorithms to analyze, optimize, and improve the reliability of subsea fiber optic infrastructure.
• Lead R&D initiatives involving predictive analytics, anomaly detection, subsea route optimization, risk modeling, and infrastructure