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About Moative

Moative, an Applied AI Services company, designs AI roadmaps, builds co-pilots and predictive AI solutions for companies in energy, utilities, packaging, commerce, and other primary industries. Through Moative Labs, we aspire to build micro-products and launch AI startups in vertical markets.

Our Past: We have built and sold two companies, one of which was an AI company. Our founders and leaders are Math PhDs, Ivy League University Alumni, Ex-Googlers, and successful entrepreneurs. 

Role

We seek skilled and experienced data science/machine learning professionals with a strong background in at least one of mathematics, financial engineering, and electrical engineering, to join our Energy & Utilities team. If you are interested in artificial intelligence, excited about solving real business problems in the energy and utilities industry, and keen to contribute to impactful projects, this role is for you!

Work you’ll do

As a data scientist in the energy and utilities industry, you will perform quantitative analysis and build mathematical models to forecast energy demand, supply and strategies of efficient load balancing. You will work on models for short term and long term pricing, improving operational efficiency, reducing costs, and ensuring reliable power supply. You’ll work closely with cross-functional teams to deploy these models in solutions that provide insights/ solutions to real-world business problems. You will also be involved in conducting experiments, building POCs and prototypes.

Responsibilities

• Develop and implement quantitative models for load forecasting, energy production and distribution optimization.
• Analyze historical data to identify and predict extreme events, and measure impact of extreme events. Enhance existing pricing and risk management frameworks.
• Develop and implement quantitative models for energy pricing and risk management. Monitor market conditions and adjust models as needed to ensure accuracy and effectiveness.
• Collaborate with engineering and operations teams to provide quantitative support for energy projects. Enhance existing energy management systems and develop new strategies for energy conservation.
• Maintain and improve quantitative tools and software used in energy management.
• Support end-to-end ML/ AI model lifecycle - from data preparation, data analysis and feature engineering to model development, validation and deployment
• Collaborate with domain experts, engineers, and stakeholders in translating business problems into data-driven solutions
• Document methodologies and results, present findings and communicate insights to non-technical audiences

Skills & Requirements

• Strong background in mathematics, econometrics, electrical engineering, or a related eld.
• Experience data analysis, and quantitative modeling using programming languages such as Python or R.
• Excellent analytical and problem-solving skills.
• Strong understanding and experience with data analysis, statistical and mathematical concepts and ML algorithms
• Proficiency in Python and familiarity with basic Python libraries for data analysis and ML algorithms (such as NumPy, Pandas, ScikitLearn, NLTK).
• Strong communication skills
• Strong collaboration skills, ability to work with engineering and operations teams.
• A continuous learning attitude and a problem solving mind-set

Good to have -

• Knowledge of energy markets, regulations, and utility operation.
• Working knowledge of cloud platforms (e.g., AWS, Azure, GCP).
• Broad understanding of data structures and data engineering.

Working at Moative

Moative is a young company, but we believe strongly in thinking long-term, while acting with urgency. Our ethos is rooted in innovation, efficiency and high-quality outcomes. We believe the future of work is AI-augmented and boundary less. Here are some of our guiding principles:

• Think in decades. Act in hours. As an independent company, our moat is time. While our decisions are for the long-term horizon, our execution will be fast – measured in hours and days, not weeks and months.
• Own the canvas. Throw yourself in to build, x or improve – anything that isn’t done right, irrespective of who did it. Be selfish about improving across the organization – because once the rot sets in, we waste years in surgery and recovery.
• Use data or don’t use data. Use data where you ought to but not as a ‘cover-my-back’ political tool. Be capable of making decisions with partial or limited data. Get better at intuition and pattern-matching. Whichever way you go, be mostly right about it.
• Avoid work about work. Process creeps on purpose, unless we constantly question it. We are deliberate about committing to rituals that take time away from the actual work. We truly believe that a meeting that could be an email, should be an email and you don’t need a person with the highest title to say that loud.
• High revenue per person. We work backwards from this metric. Our default is to automate instead of hiring. We multi-skill our people to own more outcomes than hiring someone who has less to do. We don’t like squatting and hoarding that comes in the form of hiring for growth. High revenue per person comes from high quality work from everyone. We demand it.

If this role and our work is of interest to you, please apply here. We encourage you to apply even if you believe you do not meet all the requirements listed above.

That said, you should demonstrate that you are in the 90th percentile or above. This may mean that you have studied in top-notch institutions, won competitions that are intellectually demanding, built something of your own, or rated as an outstanding performer by your current or previous employers.

The position is based out of Chennai. Our work currently involves significant in-person collaboration and we expect you to work out of our offices in Chennai.

• 3+ years experience in practical implementation and deployment of ML based systems preferred.
• BE/B Tech or M Tech (preferred) in CS/Engineering with strong mathematical/statistical background
• Strong mathematical and analytical skills, especially statistical and ML techniques, with familiarity with different supervised and unsupervised learning algorithms
• Implementation experiences and deep knowledge of Classification, Time Series Analysis, Pattern Recognition, Reinforcement Learning, Deep Learning, Dynamic Programming and Optimisation
• Experience in working on modeling graph structures related to spatiotemporal systems
• Programming skills in Python
• Experience in developing and deploying on cloud (AWS or Google or Azure)
• Good verbal and written communication skills
• Familiarity with well-known ML frameworks such as Pandas, Keras, TensorFlow