Job Summary

Startup Inno is seeking a highly analytical and mission-driven Data Scientist specializing in Sustainability & Renewable Energy Models. In this role, you will design, develop, and deploy advanced data-driven solutions that support clean energy initiatives, carbon reduction strategies, and sustainable business operations. You will collaborate cross-functionally with engineering, product, and sustainability teams to transform complex datasets into actionable insights that drive environmental impact and business value.

Key Responsibilities

• Develop predictive and optimization models for renewable energy generation, storage, and distribution.

• Analyze large-scale environmental, climate, and energy consumption datasets to identify trends and opportunities.

• Build machine learning and statistical models to support sustainability initiatives and carbon footprint reduction.

• Design data pipelines and automate data workflows for real-time and batch processing.

• Collaborate with product managers and domain experts to translate business problems into analytical solutions.

• Create dashboards and visualizations to communicate insights to technical and non-technical stakeholders.

• Monitor model performance and implement continuous improvements.

• Stay current with emerging technologies and best practices in sustainability analytics and renewable energy modeling.

Required Skills and Qualifications

• Bachelors or Masters degree in Data Science, Computer Science, Statistics, Environmental Engineering, Energy Systems, or related field.

• Strong programming skills in Python or R.

• Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch).

• Proficiency in SQL and working with large datasets.

• Experience with data visualization tools such as Tableau, Power BI, or Matplotlib.

• Solid understanding of statistical analysis and predictive modeling techniques.

• Knowledge of cloud platforms (AWS, Azure, or GCP) is preferred.

• Strong problem-solving and analytical thinking abilities.

Experience

• 3+ years of experience in data science, preferably in energy, utilities, climate tech, or sustainability domains.

• Proven experience building and deploying production-grade machine learning models.

• Experience working with time-series data and forecasting models is highly desirable.

• Exposure to renewable energy systems (solar, wind, grid analytics) is a strong advantage.

Working Hours

• Full-time position.

• Flexible working hours with core collaboration windows.

• Remote or hybrid work options available depending on location and project needs.

Knowledge, Skills, and Abilities

• Strong quantitative and statistical reasoning.

• Ability to translate complex data into clear business insights.

• Familiarity with sustainability metrics, carbon accounting, or energy market dynamics.

• Excellent communication and stakeholder management skills.

• Ability to work independently in a fast-paced startup environment.

• Strong attention to detail and commitment to data quality.

• Passion for climate tech and environmental impact.

Benefits

• Competitive salary and performance-based incentives.

• Health and wellness benefits.

• Flexible work arrangements.

• Professional development and learning budget.

• Opportunity to work on high-impact sustainability initiatives.

• Collaborative and innovative startup culture.

• Paid time off and company holidays.

Why Join Startup Inno

At Startup Inno, you will be part of a purpose-driven team focused on accelerating the transition to a sustainable future. We combine cutting-edge data science with real-world energy challenges to create meaningful environmental impact. If you are passionate about using data to power the clean energy revolution and want to grow in a fast-moving, innovation-first environment, this is the place for you.

How to Apply

Interested candidates should submit their updated resume, portfolio (if available), and a brief cover letter highlighting relevant experience in sustainability or renewable energy analytics. Applications will be reviewed on a rolling basis. Qualified candidates will be contacted for the next steps in the selection process.