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Principal Data Scientist

Come work alongside some of the most talented minds in the agtech industry. We are a team of innovators who are accelerating the digitization and sustainability of our planet’s food system. At Arable, you will have the unique opportunity to build something meaningful with an amazing group of people who care about each other and their work.


What we do:


At Arable, our goal is to connect all the world’s farms to help optimize the global food system. This is an ambitious goal, but the need has never been greater to rethink how we will feed an ever-growing population and reduce our impact on natural resources. We believe the heart of the solution is digitizing the analog world with high-fidelity data to help food producers optimize their operations. We hope the impact of our work will improve the lives of farmers everywhere and be a major contribution to securing the global food supply for decades to come.

A few examples of the work we’re doing today:


Helping farmers in India and Mozambique adapt to the effects of climate change on their farms with novel data-driven crop insurance

Giving produce growers in California the tools to optimize production and minimize waste

Helping irrigated farms in Nebraska manage water more efficiently and sustainably to protect our water supply



What We're Looking For:

Arable Labs seeks an experienced and insightful Principal Data Scientist to join our mission-driven team, reporting to the Head of Data Science. Our work leverages unique, high-fidelity field data to provide critical insights for global agriculture and environmental monitoring. In this key role, you will apply your deep expertise in machine learning, statistical modeling, and software engineering to solve complex challenges in agricultural water management. You will lead the development of core predictive models, drive innovation through applied research, and see your work contribute directly to farm sustainability and resource efficiency, often supported by broader corporate environmental initiatives. We need a hands-on technical leader passionate about tackling meaningful problems and translating data into real-world impact.


Where You'll Make an Impact:
  • Significantly improve models that help farmers optimize irrigation, conserve water, and understand field conditions (e.g., rainfall, evapotranspiration, water balance).
  • Advance Arable's predictive capabilities through the application of novel ML techniques and sensor data analysis.
  • Contribute directly to tools supporting climate resilience and sustainable practices in agriculture.


What You Will Do:
  • Lead End-to-End Model Development: Drive the full lifecycle of core machine learning models – from research, prototyping, and validation to deployment (Python, Docker, Flask, AWS/SageMaker) and ongoing performance monitoring and improvement. Key areas include water balance, ET, rainfall, and irrigation insights.
  • Conduct Applied Research & Innovation: Identify opportunities and execute applied R&D projects to enhance model accuracy, leverage new data sources (internal sensor streams, external weather data), and develop novel predictive features, balancing exploration with pragmatic delivery.
  • Collaborate for Impact: Work closely with cross-functional teams – Product (defining requirements, translating features), Sensors/IoT (understanding data, calibration and validation), and Software (API integration, production pipelines) – to ensure data science solutions effectively meet business and user needs.
  • Ensure Solution Quality & Provide Expertise: Uphold high standards for model performance and data integrity through rigorous validation, anomaly detection, and addressing operational analytical needs. Serve as a subject matter expert in your domain areas and contribute to the team's technical strategy and best practices, potentially mentoring junior members.


Required Experience and Skills:
  • MS or PhD in a quantitative field or equivalent deep practical experience.
  • 5-8+ years relevant hands-on experience developing & deploying ML/DS solutions.
  • ML & Statistical Depth: Strong theoretical understanding and practical expertise in machine learning (especially time-series), statistical modeling, and validation techniques.
  • R&D Acumen: Demonstrated ability to conduct applied research, tackle ambiguous problems, and deliver impactful, data-driven solutions.
  • Technical Implementation: Proficiency in Python for data science (NumPy, pandas, scikit-learn, etc.), strong software engineering practices (Git, testing, docs), and experience deploying models via APIs (Flask) using containers (Docker) on cloud platforms (AWS).
  • Communication & Collaboration: Excellent ability to communicate complex concepts clearly and collaborate effectively within a cross-functional environment.


Preferred Experience and Skills:
  • Domain Knowledge: Background or strong interest in agriculture, hydrology, meteorology, soil science, or related environmental sciences.
  • Sensor Data & Techniques: Experience with real-world IoT sensor data (including CalVal), anomaly detection, and leveraging external datasets (weather, geospatial).
  • Startup Environment: Proven ability to thrive and take ownership in a fast-paced, dynamic startup setting.
  • AWS ML Ecosystem: Deep familiarity with AWS services, particularly SageMaker.
  • Mentoring: Experience guiding or mentoring other technical team members.


Location:

United States based; SF Bay Area preferred, but remote US is possible.


What we offer:


At Arable, you will be joining a company of dedicated team players who bring together diverse expertise and a passion for building a more sustainable future. We are a fast-moving startup committed to providing a rewarding employee experience through the work we do, the team, compensation, and benefits, including:


Excellent medical, dental, vision, and a 401k program

Flexible PTO

A focus on community involvement and career development

Being a part of creating innovative new products that have a positive impact on the world


At Arable, we don't just accept difference—we celebrate and support it. Not only because it's the right thing to do but because we draw on the differences in who we are, what we've experienced, and how we think to make Arable thrive. Arable is proud to be an equal-opportunity workplace and is an affirmative-action employer. We are committed to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, gender expression, protected veteran status, and any other characteristic protected under applicable State or Federal laws and regulations.

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What You Should Know About Principal Data Scientist, Arable

Join Arable as a Principal Data Scientist and make a difference in the agtech industry! Located in the vibrant San Francisco Bay area, you'll collaborate with a passionate team committed to digitizing and enhancing the global food system. At Arable, we understand the importance of connecting farms worldwide to tackle the pressing challenges of food production and sustainability. As a Principal Data Scientist, you will leverage high-fidelity data, applying your expertise in machine learning and statistical modeling to solve essential problems in agricultural water management. You'll oversee the entire lifecycle of core machine learning models, from initial research and prototyping to deployment and ongoing refinement. We're looking for someone who thrives in a hands-on, innovative role, collaborating with cross-functional teams to ensure our solutions meet real-world needs. You’ll have the chance to create impactful tools that assist farmers in optimizing irrigation, conserving water, and implementing climate-resilient practices. Arable is dedicated to fostering a collaborative and supportive environment, where your contributions directly enhance the lives of farmers globally and help secure the future of food production. If you are excited about using technology to make a positive impact on the environment and agricultural practices, we want to hear from you!

Frequently Asked Questions (FAQs) for Principal Data Scientist Role at Arable
What are the main responsibilities of a Principal Data Scientist at Arable?

As a Principal Data Scientist at Arable, you will drive the development of core machine learning models, conduct applied research to improve model accuracy, and collaborate with various teams to ensure solutions meet business needs. You will work on optimizing agricultural water management through high-fidelity data analysis and lead the end-to-end model development process.

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What qualifications do I need to apply for the Principal Data Scientist position at Arable?

To apply for the Principal Data Scientist position at Arable, candidates should hold an MS or PhD in a quantitative field, or possess equivalent practical experience. Additionally, having 5-8+ years of hands-on experience in developing and deploying ML/DS solutions is crucial, along with strong skills in Python, statistical modeling, and machine learning techniques.

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What makes Arable's approach to data science unique for the agricultural sector?

Arable's approach to data science stands out due to its commitment to leveraging high-fidelity field data and innovative machine learning techniques to provide critical insights for sustainable agricultural practices. The focus on enhancing crop resilience and optimizing resource usage positions Arable as a leader in the agtech field, positively impacting farmers worldwide.

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What kind of projects can I expect to work on as a Principal Data Scientist at Arable?

As a Principal Data Scientist at Arable, you can expect to work on exciting projects that involve predicting agricultural water needs, optimizing irrigation practices, and contributing to tools that support climate resilience. You will be at the forefront of developing predictive models that have a direct impact on sustainable farming and resource efficiency.

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How does collaboration work for a Principal Data Scientist at Arable?

Collaboration is key for a Principal Data Scientist at Arable. You will engage closely with cross-functional teams, including Product, Sensors/IoT, and Software, to define requirements and ensure that data science solutions effectively address user needs while maintaining high performance standards for model accuracy and integrity.

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Common Interview Questions for Principal Data Scientist
Can you describe a machine learning project you led and the impact it had?

When answering this question, it's important to outline your role, the challenges you faced, the data sources you used, and the final outcomes. Highlight any significant improvements in prediction accuracy or operational efficiency that resulted from the project, as well as how your contributions positively impacted stakeholders or end users.

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How do you ensure the quality and integrity of your machine learning models?

To ensure quality and integrity, I implement a rigorous validation process, which includes anomaly detection and thorough testing. I regularly monitor model performance and continuously seek ways to improve accuracy by exploring new data sources and features. Being transparent and collaborative with team members also enhances model credibility.

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What machine learning techniques do you find most effective for time-series data?

Effective techniques for time-series data include ARIMA, LSTM networks, and seasonal decomposition methods. When discussing these techniques, explain why each is suited for temporal data, share specific examples from projects you've worked on, and the results they achieved for better prediction accuracy.

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How do you approach applied research when working on ambiguous problems?

I start by conducting a thorough literature review to gather insights and best practices related to the problem. Next, I carve out small, manageable experiments that allow me to test hypotheses and learn iteratively. Collaborating with domain experts and stakeholders also helps to refine the research direction and application.

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What tools and technologies are you proficient in for data analysis?

I am proficient in Python, utilizing libraries such as NumPy, pandas, and scikit-learn for data analysis. Additionally, I have experience using Docker for containerization, AWS for model deployment, and Flask for API creation, ensuring I can develop and deploy models efficiently within the cloud ecosystem.

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How do you stay current with advancements in machine learning and data science?

I regularly participate in professional conferences, follow relevant publications, and engage in online courses or workshops on new technologies and methods. Networking with other professionals in the field also allows me to share insights and stay informed about the latest trends and innovations in machine learning and data science.

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Describe your experience with cross-functional team collaboration.

Cross-functional team collaboration is crucial in delivering effective data science solutions. I ensure open lines of communication by setting regular meetings and using collaborative tools, such as project management software. In my previous roles, I've partnered with product managers and engineers to ensure data-driven insights translate into actionable features.

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What strategies do you implement for handling large datasets?

When handling large datasets, I prioritize data preprocessing to clean and optimize the data before analysis. Utilizing efficient data storage solutions, such as cloud databases, and employing distributed computing frameworks like Dask or PySpark can enhance processing speed and make the analysis more manageable.

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How do you approach mentoring junior data scientists?

Mentoring junior data scientists involves providing guidance on technical skills, sharing best practices, and fostering a supportive learning environment. I encourage them to take ownership of projects while offering constructive feedback and involving them in strategic discussions to help them grow in their roles.

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What are your thoughts on the future of data science in agriculture?

The future of data science in agriculture is incredibly promising. As we face challenges related to climate change and food security, data-driven solutions will play a critical role in optimizing resources and improving crop yield. Innovations in sensor technology and machine learning will increasingly empower farmers to make informed decisions, enhancing sustainability and resilience in agriculture.

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People can solve natural resource challenges if they possess the right tools. Arable enables data-driven decisions in agriculture and natural resource management using Measurements that Matter. With real-time, continuous visibility and predictive...

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Full-time, remote
DATE POSTED
April 5, 2025

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