Data Scientist

Aboitiz Data Innovation (ADI) is one of the leading up and coming start-ups in the field of Data Science and Artificial Intelligence. We believe data can drive change for a better world by advancing businesses across industries and communities.

We are seeking a highly skilled and motivated Data Scientist to join our team in the industrial and power generation sector. This role requires a strong analytical mind, independent thinking, and a collaborative approach to problem-solving. The ideal candidate will have a deep understanding of data science, machine learning, and computational modeling, along with industry-specific knowledge in physics, material science, and engineering systems.

The successful candidate will work with a wide range of stakeholders, including engineers, operations managers, maintenance teams, and business strategists, to develop data-driven solutions that enhance efficiency, reliability, and predictive maintenance of power generation assets.

1. Data Analysis and Modeling:
   1. Develop and apply advanced statistical, machine learning, and optimization models to analyze large datasets from industrial and power generation systems.
   2. Identify patterns, trends, and anomalies in data to improve operational efficiency, predict equipment failures, and optimize energy consumption.
2. Predictive Maintenance:
   1. Design and implement predictive maintenance models to reduce downtime and extend the lifespan of critical equipment.
   2. Collaborate with engineering teams to integrate predictive analytics into maintenance workflows.
3. Process Optimization:
   1. Use data-driven approaches to optimize industrial processes, such as production lines, energy generation, and resource allocation.
   2. Apply simulation and optimization techniques to improve system performance and reduce costs.
4. Stakeholder Collaboration:
   1. Work closely with cross-functional teams to understand business needs and translate them into data science solutions.
   2. Communicate complex analytical findings to non-technical stakeholders in a clear and actionable manner.
5. Data Infrastructure and Tools:
   1. Develop and maintain scalable data pipelines and workflows to support analytics and modeling efforts.
   2. Evaluate and implement new tools, frameworks, and technologies to enhance data science capabilities.
6. Research and Innovation:
   1. Stay up-to-date with the latest advancements in data science, industrial IoT, and power generation technologies.
   2. Explore innovative applications of AI/ML in areas such as renewable energy integration, grid stability, and material science.

1. Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch) and SQL.
2. Experience with big data frameworks (Apache Spark, Hadoop) and cloud platforms (AWS, Azure, GCP).
3. Expertise in machine learning, deep learning, and reinforcement learning.
4. Proficiency in time-series forecasting techniques (ARIMA, LSTMs, Prophet) for power system analytics.
5. Knowledge of Bayesian statistics and probabilistic modeling.
6. Familiarity with physics-informed machine learning and computational simulations.
7. Experience with graph analytics and network modeling for power grid analysis.
8. Strong grasp of data visualization tools (Tableau, Power BI, Plotly, Matplotlib, Seaborn).
9. Understanding of SCADA systems, industrial automation protocols, and sensor networks is a plus.

1. Strong problem-solving abilities and critical thinking skills.
2. Excellent communication skills, with the ability to explain complex models to non-technical stakeholders.
3. Ability to work independently and collaboratively in a fast-paced environment.
4. Proactive in learning and keeping up with emerging technologies in the industrial AI space.

1. Bachelor's degree (minimum) in Data Science, Computer Science, Engineering, Physics, Material Science, or a related field.
2. Open to fresh graduates with strong technical experience and a portfolio of projects demonstrating expertise in data science and machine learning.
3. Master's or Ph.D. in a relevant field is considered a plus.
4. Experience in the power generation sector, renewable energy analytics, or industrial maintenance.
5. Background in numerical modeling (Finite Element Analysis, Computational Fluid Dynamics).
6. Exposure to Digital Twins and Industrial AI applications.
7. Hands-on experience with AI-driven process optimization and control systems.