Data Scientist (AI Engineer)

Tel Aviv
11 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist - New

Data Scientist / Software Engineer

Data Scientist - Contract - 12 months

Data Scientist (AI Engineer)
Salary dependent on skills and experience plus share options in a hyper-growth startup.
Hybrid – office in Central Tel Aviv.
The Company
Streamlining business development for Dealmakers.
This SaaS startup aims to tackle inefficiencies, disconnected systems, and missed opportunities to help organisations grow smarter and faster. With innovation at its core, the platform strives to simplify business growth using cutting-edge AI technology.
The Role
Join a small, dynamic, and growing team of experienced professionals dedicated to revolutionising business development. As the Founding AI Engineer, you will work closely with the co-founders to rapidly iterate on product ideas and help shape the technical vision from the ground up. This is a hands-on role that requires deep technical expertise, entrepreneurial drive, and the ability to turn ideas into scalable solutions.
You’ll have the opportunity to build the product from the earliest stages, solving real customer problems, and playing a key role in the company’s journey towards achieving its business milestones.
Key Responsibilities but not limited to:

  • Collaborate with cross-functional teams to identify business opportunities and design data-driven solutions to address them.
  • Develop, fine-tune, and deploy machine learning and deep learning models, including neural networks, that enhance the platform’s insights and intelligence.
  • Integrate large language models (LLMs) into our product pipeline, exploring cutting-edge techniques such as Retrieval-Augmented Generation (RAG) for improved data insights and user interaction.
  • Leverage LLM frameworks like LangChain to build and manage LLM-based workflows, adapting pipelines to respond to evolving data and user needs.
  • Perform exploratory data analysis, data processing, and feature engineering to support model building.
  • Partner with engineering teams to integrate data solutions into the product, ensuring scalable and reliable deployment.
  • Create and manage data pipelines, ensuring data integrity, quality, and compliance with industry standards (SOC 2, ISO 27001).
  • Conduct experiments, validate hypotheses, and iteratively improve models based on real-world feedback.
  • Communicate findings and insights to non-technical stakeholders to inform decision-making.
  • Establish best practices in data science, data science, ML/AI, and deep learning, setting standards for a growing data team.
    Key Skills:
  • Fluency in English.
  • Minimum 3-5 years of professional or academic experience in data science, machine learning, AI, deep learning etc.
  • Strong proficiency in Python, with experience using machine learning and deep learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
  • Familiarity with key machine learning algorithms, including but not limited to decision trees, gradient boosting, clustering, and neural networks for complex data modelling.
  • Practical experience deploying AI/ML models, including LLMs, using techniques such as RAG, fine-tuning, and prompt engineering.
  • Familiarity with LLM pipelines and frameworks such as LangChain to enhance product capabilities and model integration.
  • Strong experience with data analytics, statistical modelling, and predictive analytics.
  • Solid understanding of SQL and experience with relational and non-relational databases; familiarity with cloud data solutions (e.g., AWS Redshift, Azure Synapse) is a plus.
  • Experience working with large datasets and data pipeline frameworks (e.g., Spark, Airflow).
  • Knowledge of cloud platforms (AWS, Azure) and scalable infrastructure for ML, deep learning, and LLM pipelines.
  • Experience with LLMs, NLP, LLM techniques such as RAG.
  • Bonus: Interest or experience in M&A, finance or business strategy.
  • An entrepreneurial mindset with a passion for using data to drive innovation and solve real business challenges.
    Why Join us?
    This role is ideal for someone who is excited about building a product from the ground up, working in a fast-moving startup, and solving real customer problems. You'llhave the opportunity to grow within the company as we scale and make a meaningful impact on the future of dealmaking.
    Interested? If you feel that you possess the relevant skills and experience, then please submit your CV.
    INDHS

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.