Scope of position and background info:
Hybrid – Office
Responsibilities:
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.
Requirements:
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.
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