WorkOnGrid

We are seeking a passionate and detail-oriented Technical Support Engineer to join our growing team. In this role, you'll work closely with customers and internal teams to troubleshoot, debug, and resolve technical issues related to our APIs and Python-based integrations. Your ability to understand code and APIs, communicate clearly, and solve problems efficiently will be key to success. Responsibilities Provide technical support for our RESTful APIs, SDKs, and Python-based applications. Investigate and troubleshoot customer-reported issues and guide them toward resolution. Debug and analyze logs, scripts, and system behavior to resolve integration problems. Collaborate with engineering and product teams to report bugs and suggest improvements. Create and maintain support documentation, FAQs, and internal knowledge bases. Act as a bridge between customers and internal teams to ensure smooth issue resolution. Requirements 1-2 years of experience in a technical support, software engineering, or customer-facing engineering role. Strong working knowledge of Python. Good understanding of REST APIs, HTTP methods, and common integration challenges. Experience with tools like Postman, cURL, and JSON parsing. Basic understanding of authentication mechanisms like OAuth 2.0 API keys, etc. Strong communication skills and a customer-first mindset. This job was posted by Athmiya Hm from WorkOnGrid.

About GRID: We are pioneering the future of Operations Intelligence for the utilities. Our platform helps organizations optimize operations, enhance efficiency, and drive innovation through cutting-edge technology. With our platform, we are changing the way utilities deploy field operations, automation and data management applications on the distribution side. About the Role: We are looking for a passionate Data Scientist with 3–5 years of experience in the energy sector to join our team. In this role, you will leverage advanced data science, machine learning, and deep learning techniques to solve real-world challenges in energy demand forecasting, smart grid optimization, renewable integration, and asset performance. You will work closely with business stakeholders, domain experts, and data engineers to turn complex datasets into actionable insights that drive efficiency, sustainability, and innovation in the energy industry. This role offers the opportunity to contribute directly to the transformation of the energy sector through AI-driven solutions. Responsibilities: Collaborate with business stakeholders and domain experts to understand challenges and opportunities within the energy sector. Design and develop machine learning and deep learning models for energy applications such as demand forecasting, grid management, renewable integration, and equipment optimization. Acquire and prepare data from diverse sources (IoT sensors, smart meters, SCADA systems, weather data, and market prices) ensuring quality and readiness for analysis. Conduct EDA to identify patterns, correlations, and anomalies that drive actionable insights. Implement data science solutions using regression, classification, clustering, and neural networks to improve decision-making and operational efficiency. Evaluate model performance with appropriate metrics and continuously refine models for accuracy and reliability. Develop clear dashboards, reports, and data visualizations to communicate findings to technical and non-technical stakeholders. Stay updated with emerging trends in AI, IoT, renewable energy technologies, and energy analytics. Collaborate with Data Engineers to enhance data infrastructure, collection strategies, and model deployment processes. Provide technical guidance and mentor junior data scientists or analysts when required. Requirement: Master’s in Data Science, Computer Science, Engineering, or related quantitative field. 3–5 years of experience as a Data Scientist, with focus in the energy/utilities/renewables domain. Strong programming skills in Python, with hands-on experience using pandas, scikit-learn, TensorFlow, PyTorch, or Keras. Proficiency in SQL for data querying and transformation. Strong foundation in machine learning algorithms, statistical analysis, and deep learning frameworks. Experience with data visualization tools (Tableau, Power BI, etc.) Familiarity with big data technologies (Hadoop, Apache Spark, Databricks) and cloud platforms (AWS, Azure, GCP). Domain expertise in areas like renewable energy, power generation, smart grids, oil & gas exploration, or energy trading. Strong analytical, problem-solving, and critical-thinking skills with experience working on large, complex datasets. Excellent written and verbal communication skills.