Python (or R)

• pandas for cleaning, reshaping and joining data; numpy for the numeric work
• Reads and writes CSV, Excel, Parquet and JSON, and can pull data from a REST API
• Works comfortably in notebooks, but can also write a plain.py script that runs on a schedule
• Familiar with scikit-learn or stats models for regression and forecasting

Statistics and Modelling

• Solid descriptive statistics, and the judgement to know when the mean is misleading
• Understands sampling, bias, correlation vs causation, and confidence intervals
• Can run and interpret a hypothesis test or an A/B comparison
• Linear and logistic regression — fit one, and explain what the coefficients mean in plain English
• Knows when not to model, and will say “the data can't answer that”

Other Data Skills

• Apply agronomic knowledge and data science principles and techniques to enhance the accuracy and reliability of yield forecasts.
• Drive the agronomy and forecasting narrative during customer discussions, effectively communicating forecasting methodologies, results, and ensuring a clear and shared understanding.
• Actively listen to customer feedback, including insights from agronomists and operational leads, and integrate this information into forecast adjustments.
• Maintain a keen eye for detail in data analysis, model validation, and report generation.
• Effectively communicate technical information to both technical and non-technical audiences.
• Other duties as directed.

Working with AI tools

We expect this person to use AI tools as part of normal work, and to use them well.

• Uses an assistant such as Claude, ChatGPT or Copilot day to day to draft and debug code, explain unfamiliar SQL or libraries, scaffold an approach to a problem, translate between languages, and write documentation
• Writes good prompts — supplies the schema, the constraints and a sample of the data rather than asking a vague question
• Verifies everything. Runs the code, checks the numbers against a known total, and never pastes an AI-generated figure into a report without confirming it. Understands that a confident answer can still be wrong
• Uses AI to get up to speed on an unfamiliar technique, then genuinely understands it — not a black box they can't defend in a meeting
• Applies judgement about what data goes into which tool, and follows our data-handling policy

Customer Relations

• Work collaboratively with Bitwise customers to enable them to understand the value in their data.
• Respond to customer technical queries and concerns about the forecasts as appropriate in a timely and efficient manner.
• Together with the Customer Success Manager, build and maintain relationships with the stakeholders to ensure the resolution of data issues and queries in a timely manner.
• Work and consult with management and team members across a range of projects to meet business goals and objectives.
• Other duties as directed.

Position Requirements

Qualifications

• Bachelors or Masters’ degree in Agronomy, Agricultural Science, Data Science, Statistics, and/or a related field is preferred.

Experience / Knowledge

• Hands-on experience in commercial berry production
• Strong understanding of the key drivers influencing crop yield (e.g., growing degree days, temperature, solar radiation, fruit development).
• High level of proficiency in Google Sheet/Microsoft Excel and python
• Experience in using Python and AI tools (such as Claude) for data analysis and model development
• Experience in crop yield forecasting is highly desired

Skills & Abilities

• Fluency in English is essential, it does not need to be the primary language of the successful application, but proficiency is critical.
• Excellent communication, presentation, and interpersonal skills, with the ability to confidently lead customer meetings.
• Strong analytical and problem-solving abilities with meticulous attention to detail.
• Ability to listen actively and interpret information from diverse stakeholders.
• Flexibility to work across a range of time zones, and therefore a variety of hours of work.
• Being adaptive and comfortable working in a fast-paced, evolving environment.