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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, nancial engineering, and electrical engineering, to join our Energy & Utilities team. If you are interested in articial 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 ecient load balancing. You will work on models for short term and long term pricing, improving operational eciency, 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 ndings 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, eciency 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 selsh 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.

Roles & Responsibilities:

-Adopt novel and breakthrough Deep Learning/Machine Learning technology to fully solve real world problems for different industries. -Develop prototypes of machine learning models based on existing research papers.

-Utilize published/existing models to meet business requirements. Tweak existing implementations to improve efficiencies and adapt for use-case variations.

-Optimize machine learning model training and inference time. -Work closely with development and QA teams in transitioning prototypes to commercial products

-Independently work end-to-end from data collection, preparation/annotation to validation of outcomes.

-Define and develop ML infrastructure to improve efficiency of ML development workflows.

Must Have:

- Experience in productizing and deployment of ML solutions.

- AI/ML expertise areas: Computer Vision with Deep Learning. Experience with object detection, classification, recognition; document layout and understanding tasks, OCR/ICR

. - Thorough understanding of full ML pipeline, starting from data collection to model building to inference.

- Experience with Python, OpenCV and at least a few framework/libraries (TensorFlow / Keras / PyTorch / spaCy / fastText / Scikit-learn etc.)

- Years with relevant experience:

5+ -Experience or Knowledge in ML OPS.

Good to Have: NLP: Text classification, entity extraction, content summarization. AWS, Docker.

We are looking for an outstanding ML Architect (Deployments) with expertise in deploying Machine Learning solutions/models into production and scaling them to serve millions of customers. A candidate with an adaptable and productive working style which fits in a fast-moving environment.

Skills:

- 5+ years deploying Machine Learning pipelines in large enterprise production systems.

- Experience developing end to end ML solutions from business hypothesis to deployment / understanding the entirety of the ML development life cycle.
- Expert in modern software development practices; solid experience using source control management (CI/CD).
- Proficient in designing relevant architecture / microservices to fulfil application integration, model monitoring, training / re-training, model management, model deployment, model experimentation/development, alert mechanisms.
- Experience with public cloud platforms (Azure, AWS, GCP).
- Serverless services like lambda, azure functions, and/or cloud functions.
- Orchestration services like data factory, data pipeline, and/or data flow.
- Data science workbench/managed services like azure machine learning, sagemaker, and/or AI platform.
- Data warehouse services like snowflake, redshift, bigquery, azure sql dw, AWS Redshift.
- Distributed computing services like Pyspark, EMR, Databricks.
- Data storage services like cloud storage, S3, blob, S3 Glacier.
- Data visualization tools like Power BI, Tableau, Quicksight, and/or Qlik.
- Proven experience serving up predictive algorithms and analytics through batch and real-time APIs.
- Solid working experience with software engineers, data scientists, product owners, business analysts, project managers, and business stakeholders to design the holistic solution.
- Strong technical acumen around automated testing.
- Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc.)
- Strong hands-on experience with statistical packages and ML libraries (e.g., Python scikit learn, Spark MLlib, etc.)
- Experience in effective data exploration and visualization (e.g., Excel, Power BI, Tableau, Qlik, etc.)
- Experience in developing and debugging in one or more of the languages Java, Python.
- Ability to work in cross functional teams.
- Apply Machine Learning techniques in production including, but not limited to, neuralnets, regression, decision trees, random forests, ensembles, SVM, Bayesian models, K-Means, etc.

Roles and Responsibilities:

Deploying ML models into production, and scaling them to serve millions of customers.

Technical solutioning skills with deep understanding of technical API integrations, AI / Data Science, BigData and public cloud architectures / deployments in a SaaS environment.

Strong stakeholder relationship management skills - able to influence and manage the expectations of senior executives.
Strong networking skills with the ability to build and maintain strong relationships with both business, operations and technology teams internally and externally.

Provide software design and programming support to projects.

 Qualifications & Experience:

Engineering and post graduate candidates, preferably in Computer Science, from premier institutions with proven work experience as a Machine Learning Architect (Deployments) or a similar role for 5-7 years.