Location: Delhi NCR, India (Hybrid)

Employment Type: Full-Time

Company: Takachar

ABOUT US

Takachar develops small-scale, low-cost, portable thermochemical reactors that convert agricultural and forestry residues into valuable bioproducts, including biochar and fuels. Our technology makes it possible to process biomass closer to where it is generated, creating economic opportunities for rural communities while reducing open burning, air pollution, and wildfire risks. We have deployed our technology in real-world operating environments and are now working to make our next generation of reactors more intelligent, adaptable, and efficient through advanced data science and machine learning.

Our work sits at the intersection of climate technology, physical sciences, artificial intelligence, and real-world engineering.

THE OPPORTUNITY

We are looking for a hands-on principal data scientist to lead the development of data-driven intelligence and optimization capabilities for our thermochemical reactor technology. Unlike conventional AI applications built on large, clean datasets, our systems operate in complex physical environments with variable feedstocks, imperfect measurements, and operational constraints.

The challenge is to translate experimental and operational data into reliable predictions, actionable insights, and improved process performance.

You will work at the intersection of machine learning, statistical modeling, physical sciences, and engineering. We are particularly interested in candidates who enjoy solving difficult, open-ended technical problems and who can exercise sound judgment about when sophisticated AI methods are justified—and when simpler approaches work better.

This is a role with substantial ownership. You will help determine not only how technical problems are solved, but also which problems are worth solving .

WHAT YOU WILL OWN1. Applied AI/ML and Predictive Modeling

- Develop and validate predictive models linking reactor operating conditions, feedstock characteristics, and process outcomes.
- Apply statistical modeling, machine learning, optimization, and other computational methods to complex physical systems.
- Explore opportunities to combine data-driven approaches with relevant physical and engineering principles.
- Work with noisy, incomplete, and heterogeneous datasets, addressing uncertainty, model robustness, and generalization.
- Benchmark alternative modeling approaches and select methods based on performance, interpretability, and practical value.
- Translate modeling results into actionable recommendations for engineering and operations teams.

1. Process Optimization and Experimental Validation

- Collaborate with engineers and scientists to design meaningful experiments and evaluate process behavior.
- Identify influential operating variables and opportunities to improve reactor performance, reliability, and efficiency.
- Develop analytical approaches to support operating decisions and process optimization.
- Validate proposed improvements against experimental and field performance.




- Connect technical work to measurable engineering, operational, and commercial outcomes.

1. Data Science Systems and Deployment

- Develop reproducible workflows for processing experimental, operational, and laboratory data.
- Collaborate with our engineering team to improve data quality, instrumentation requirements, and analytical capabilities.
- Build and maintain reliable ML workflows, including model evaluation, deployment, and performance monitoring.
- Develop practical analytical tools and decision-support capabilities for engineering and field operations.
- Apply emerging AI technologies where they meaningfully improve technical productivity or operational performance.
- Support data integrity and traceability requirements relevant to our carbon-removal activities.

1. Technical Leadership and Strategy

- Own the applied AI/ML roadmap, translating company objectives into explicit priorities, milestones, and deliverables.
- Independently evaluate technical approaches, make informed decisions, and take accountability for outcomes.
- Work across R&D;, electrical engineering, operations, and commercial teams to identify and execute high-value opportunities.
- Communicate technical results, uncertainties, trade-offs, and risks clearly to colleagues from different backgrounds.
- Establish good practices for reproducible development, technical documentation, and model validation.
- Remain personally hands-on with implementation, experimentation, and problem-solving.
- Help build and mentor a growing technical team as the company scales.

WHO WE ARE LOOKING FOR We welcome candidates from applied machine learning, industrial analytics, scientific computing, energy systems, engineering, and other quantitative backgrounds.

Prior experience in biomass conversion or carbon markets is welcome but not required. What matters more is your ability to learn unfamiliar physical systems, formulate technical approaches, and deliver useful results.

Required Qualifications

- Substantial hands-on experience developing and deploying machine-learning or advanced statistical models to solve real-world problems.
- Strong Python programming skills and proficiency with common scientific computing and ML libraries.
- Solid understanding of statistical analysis, model validation, feature engineering, and optimization.
- Experience working with imperfect real-world data and evaluating model uncertainty or limitations.
- Demonstrated ability to independently lead technical projects from problem definition through implementation and validation.
- Sound technical judgment, including the ability to balance complexity, accuracy, computational requirements, and practical constraints.




- Experience collaborating effectively with multidisciplinary teams and translating technical work into measurable outcomes.
- Strong communication skills and the ability to explain technical decisions to non-specialists.
- Willingness to remain hands-on while taking responsibility for technical strategy and delivery.
- Comfort working in a fast-moving environment where objectives may be open-ended and priorities can evolve.

We value demonstrated technical ownership and accomplishments more than specific years of experience or job titles.

Preferred Qualifications

- Experience with scientific machine learning, physics-informed modeling, or hybrid physics/data-driven approaches.
- Familiarity with time-series analysis, system identification, design of experiments, or control and optimization.
- Experience with industrial IoT, process engineering, manufacturing, or other physical systems.
- Experience implementing production ML systems and associated MLOps practices.
- Background in applied mathematics, physics, computer science, chemical engineering, mechanical engineering, or a related quantitative discipline.
- Experience leading or mentoring technical contributors.
- Exposure to energy, climate technology, biomass conversion, or carbon-removal applications.

HOW WE WORK Takachar is an early-stage technology company. We operate in environments where scientific uncertainty, field constraints, and evolving business needs are everyday realities. We believe great technical leaders do more than execute predefined tasks.

They identify problems, establish priorities, develop credible approaches, and take responsibility for delivering results. We provide strategic direction, domain expertise, and collaborative support. In return, we expect you to exercise independent judgment and take ownership of your work.

That means being comfortable making decisions with incomplete information, challenging assumptions constructively, seeking expertise when appropriate, and adapting when evidence points toward a different approach. We value intellectual curiosity, scientific rigor, practicality, and follow-through.

This is not a pure management role, a pure data engineering role, or a position focused primarily on LLM applications. We are looking for someone who enjoys hands-on quantitative problem-solving and wants to build technology that works in the physical world.

WHY JOIN US

You will have an opportunity to apply advanced AI and data science to a challenging physical technology with real-world environmental and economic impact. Unlike many AI roles focused on digital products alone, your work will directly influence how physical equipment operates and performs in the field. You will collaborate with scientists, engineers, and commercial teams, gaining exposure to the full journey from research and experimentation to deployment and commercialization.

As Takachar grows, you will play a central role in shaping the company's technical capabilities and longer-term AI strategy.

TO APPLY

Please apply via LinkedIn. For questions, contact careers at takachar dot com.

📌 Principal Data Scientist (Delhi)
🏢 Takachar
📍 Delhi