Contribute to internal knowledge sharing and cross-functional learning through documentation, demos, and collaborative experimentation.
Stay on top of emerging methods and tools in ML, deep learning, NLP, and agentic workflows—bringing new ideas to the table and pushing the envelope of what’s possible.
Collaborate with stakeholders across R&D (Product, Engineering, Research, Design) and GTM to frame analytical problems, align on objectives, and define success metrics.
Translate ambiguous business challenges into clearly scoped, feasible data science projects.
Partner with Engineering to design and implement AI-powered product features, applying best practices around model selection, system design, optimization, and monitoring.
Evaluate LLM and generative AI features using both quantitative metrics and qualitative techniques—developing benchmarks, stress tests, and custom evaluation pipelines to ensure reliability and performance.
Work with Analytics Engineering and Data Engineering teams to source and validate high-quality data for model development.
Conduct exploratory data analysis (EDA) to surface trends, patterns, outliers, and opportunities for impact.
Requirements
llms
langchain
python
sql
aws
prompt engineering
Experience with LLMs, embeddings, and vector search tools (e.g. LangChain, OpenAI APIs, Pinecone, FAISS).
Ability to clearly communicate technical concepts to stakeholders across varying levels of technical fluency.
Comfort using tools like Jupyter, Looker, GitHub, or cloud platforms (AWS, GCP).
Strong analytical skills and experience working with large datasets using SQL, Pandas, and other data tools.
3–5+ years of experience in data science, ML, or applied statistics in a product or growth-oriented B2B SaaS environment.
Familiarity with agentic AI workflows, prompt engineering, or RAG (Retrieval-Augmented Generation) pipelines.
Experience building evaluation pipelines for ML and AI models.
Experience collaborating closely with Product, Engineering, or Marketing teams to ship user-facing AI features or data products.
Previous experience working in cross-functional environments and driving ML/AI projects from ideation to deployment.
Demonstrated ability to design, train, and validate models using structured and unstructured data.
Strong programming skills in Python and experience with ML libraries like scikit-learn, XGBoost, PyTorch, or TensorFlow.
Benefits
Information not given or found
Training + Development
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Interview process
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Visa Sponsorship
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Security clearance
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Company
Overview
€120K Funding
Pre-seed Investment
Raised approximately €120,000 in pre-seed funding in mid-2022 to validate and scale its model.
Founded in 2020 by diasporan entrepreneurs, the company bridges European capital and African SMEs through climate‑focused financing.
Its fintech platform enables last‑mile access to green equipment by pairing carbon credits with affordable loans.
Notable product lines include financing tailored to e‑mobility (“Kijani Move”), solar power equipment (“Kijani Power”), and solar irrigation or greenhouse systems (“Kijani Farm”).
Typically supports renewable energy and agricultural SMEs across Africa by reducing barriers to sustainable infrastructure investment.
Operates from hubs in Nairobi and Frankfurt, blending local African presence with international investor reach.
Unusually integrates impact measurement into credit scoring systems to facilitate sustainability-linked investment flows.
Culture + Values
A commitment to excellence drives the organization toward continuous improvement and high standards.
A focus on delivering innovative solutions ensures the organization stays at the forefront of industry advancements.
Collaboration with stakeholders fosters trust and mutual growth across all business relationships.
Empowering communities through impactful investment reflects the organization's dedication to social responsibility.
Sustainable growth and long-term value creation guide the organization's strategic planning and resource allocation.
Environment + Sustainability
Net Zero by 2035
Climate Goal
Aiming to achieve net zero emissions by 2035.
Focus on renewable energy investments
Promoting sustainable agriculture practices
Environmental risk mitigation through strategic investments
Inclusion & Diversity
Commitment to gender equality in the workplace
Diverse team with a balanced gender representation
Encouraging equal opportunities for career advancement