Job Summary
Startup Inno is seeking a highly motivated and analytical Data Scientist – Smart Grid & Load Forecasting to join our growing data and energy innovation team. In this role, you will be responsible for designing, developing, and deploying advanced predictive models to forecast electricity demand and optimize smart grid operations. You will work closely with engineers, energy analysts, and product managers to turn complex energy data into actionable insights that drive efficiency, sustainability, and cost optimization across modern power systems.
This position is ideal for professionals passionate about applying data science and machine learning to real-world challenges in the energy and utilities sector.
Key Responsibilities
Develop and maintain machine learning and statistical models for short-term and long-term load forecasting.
Analyze large-scale time-series data from smart meters, IoT devices, and grid sensors.
Build end-to-end data pipelines for data collection, cleaning, feature engineering, and model deployment.
Collaborate with engineering teams to integrate predictive models into smart grid platforms and decision-support systems.
Evaluate model performance and continuously improve accuracy and reliability.
Conduct exploratory data analysis to identify patterns, anomalies, and optimization opportunities.
Prepare technical documentation, dashboards, and reports for stakeholders.
Support energy optimization initiatives such as demand response, peak load management, and renewable integration.
Required Skills and Qualifications
Bachelors or Masters degree in Data Science, Computer Science, Statistics, Electrical Engineering, or a related field.
Strong proficiency in Python and common data science libraries (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch).
Experience with time-series analysis and forecasting techniques (ARIMA, LSTM, Prophet, XGBoost, etc.).
Solid understanding of machine learning, statistical modeling, and data visualization.
Experience with SQL and data warehousing solutions.
Familiarity with cloud platforms (AWS, Azure, or GCP) and big data tools (Spark, Hadoop) is a plus.
Strong communication skills with the ability to explain complex models to non-technical stakeholders.
Experience
2–5 years of professional experience in data science, analytics, or applied machine learning.
Prior experience in energy, utilities, smart grids, IoT, or infrastructure analytics is highly preferred.
Hands‑on experience building and deploying predictive models in a production environment.
Working Hours
Full‑time position (40 hours per week).
Flexible working hours with the option for remote or hybrid work, depending on project needs.
Occasional collaboration across global time zones.
Knowledge, Skills, and Abilities
Strong analytical and problem‑solving mindset.
Ability to work with large, complex, and noisy datasets.
High attention to detail and data quality.
Capacity to work independently and in cross‑functional teams.
Strong business acumen and understanding of how data drives strategic decisions.
Passion for sustainability, renewable energy, and digital transformation.
Benefits
Competitive salary and performance‑based bonuses.
Flexible work arrangements (remote/hybrid options).
Health insurance and wellness programs.
Learning and development budget for certifications and training.
Opportunity to work on cutting‑edge smart energy projects.
Inclusive and collaborative work culture.
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