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This role provides the opportunity to drive significant impact through scalable data solutions while working with a collaborative, growth-focused team. You'll work with cutting-edge AWS technologies, solve complex data challenges, and play a key role in shaping our data platform's future. You'll learn unique graph relations models and valuable graph database experience.

Were looking for a senior Data Scientist to lead into the next stage, utilizing our data assets for in-house business development, product development, and customer outreach.

You will work with large-scale data at the intersection of agronomy and commerce, designing and implementing algorithmic and machine learning solutions that power our data-driven platform. This will include predicting buying behaviors models, sales, and product recommendation models based on farmers' behavior, and combining this with relevant product recommendations. If youre passionate about turning complex data into real-world impact in agriculture, we are looking for you, a talented, detail-oriented person to join our team.

Were rapidly growing and looking for another important piece of the puzzle.
Is it you?
We are looking for a highly motivated candidate to join our Data Science team If you are passionate about NLP \LLMs and have 4+ years in the field, this is for you!
What Youll Do:
Research, experiment, develop and productize advanced NLP, LLM and autonomous agent solutions in various domains such as classification, summarization, translation, information retrieval and AI assistant.
Use advanced deep learning and large language models, to enhance Shields ability to find the needle in a haystack in the world of structured & unstructured data of financial electronic communication.
Conduct rigorous experimentation, analysis, and optimization to ensure performance, efficiency, and scalability.
Develop processes, tools and frameworks to monitor and analyze model performance and data accuracy
Innovate, develop, and test state-of-the-art approaches to solve our challenging problems: domain adaptation, fine-tuning, prompt engineering, zero- and few-shot learning, autonomous agents, using open and close large language models.
Assist and mentor other team members in the implementation and research
Collaborate closely with domain experts, product and engineering teams to translate research breakthroughs into scalable commercial products
Identify the latest technology to improve our AI capabilities in a variety of languages and help shaping our AI roadmap

We are looking for a We are looking for a.
As a Big Data Team Lead, you will lead a core team focused on the ingestion of big data and the development of core algorithms. Your role will involve building scalable and robust solutions for processing massive geospatial datasets, including spatially-focused algorithms. You will also be responsible for developing innovative big data solutions to ensure accuracy and efficiency within a fast-paced and evolving technical ecosystem. This position requires a passionate leader with a strong background in big data technologies and a drive to innovate.
RESPONSIBILITIES:
Lead and manage a big data engineering team, focused on processing high-volume spatial-temporal data, while also engaging in hands-on technical tasks.
Collaborate daily with product managers, R&D stakeholders, and data scientists to translate requirements into robust data systems.
Prioritize and scope incoming feature requests and technical improvements to balance innovation with stability.
Maintain and evolve the teams Apache Spark-based data pipelines, including optimization and scaling.
Lead technical planning sessions, foster ownership and accountability within the team.
Drive project-level KPIs and data quality KPIs such as pipeline completion time, job reliability, data freshness metrics and data accuracy.
Manage 36 direct reports.

Were looking for a hands-on Individual Contributor Data Engineer to design, build, and operate large-scale data products. Youll own mission-critical pipelines and services, balancing pre-computation with on-demand execution to deliver complex, business-critical insights with the right cost, latency, and reliability.
RESPONSIBILITIES:
Design and run Spark data pipelines, orchestrated with Airflow, governed with Unity Catalog.
Build scalable batch and on-demand data products, aiming for the sweet spot between pre-compute and on-demand for complex logic - owning SLAs/SLOs, cost, and performance.
Implement robust data quality, lineage, and observability across pipelines.
Contribute to the architecture and scaling of our Export Center for off-platform report generation and delivery.
Partner with Product, Analytics, and Backend to turn requirements into resilient data systems.

We are looking for a Senior Data Analyst to join the Foundation Entities team, part of the Ingestion division.
The Foundation Entities group is where data meets reality. We model POIs (venues and complexes), chains, markets, and other foundation entities, tackling the challenge of turning billions of signals into a living, evolving database of the physical world. Our work powers the insights, products, and innovations that define us.
This role goes beyond traditional analytics. You will work with large-scale datasets, apply advanced analytics and statistical methods, and design scalable solutions that replace manual processes with automated, data-driven services. You will collaborate closely with engineers, data scientists, and product stakeholders, contributing directly to the accuracy, scalability, and innovation of foundational data.
RESPONSIBILITIES:
We expect our analysts to be self-driven, capable of working independently with multiple stakeholders, and comfortable managing several projects in parallel.
Key responsibilities include:
Analyzing and improving the quality, coverage, and scalability of POIs, chains, and other foundation entities.
Developing metrics, monitoring, and validation tools to ensure data reliability and automation at scale.
Conducting applied research and testing hypotheses to replace manual workflows with data-driven processes.
Researching and integrating external data sources (business listings, APIs, open data, etc.) to enrich entity datasets.
Communicating insights and recommendations clearly to both technical and non-technical audiences.