Data Scientist/Prompt Engineer

Company Description

MenthorQ is a leading TradeTech company building AI-powered, quantitative models that turn complex financial data into simple, actionable strategies for active investors. By harnessing the power of Big Data and AI, we enable investors to analyze vast amounts of financial information quickly and effectively. We are bridging the gap between professional and retail traders.

Our international team operates from hubs in Bologna, Italy and New York, USA, bringing together trading experts and tech innovators.

Role Description

We're seeking a talented Data Scientist/Prompt Engineer to join our AI team and play a crucial role in enhancing QUIN, our revolutionary AI trading assistant. This unique position sits at the intersection of data science, AI engineering, and financial technology, where you'll optimize large language models, develop quantitative models, and craft sophisticated prompting strategies that help thousands of traders make smarter decisions.

As our Data Scientist/Prompt Engineer, you'll be instrumental in making complex market data accessible through natural language interfaces, while applying classical machine learning techniques to extract insights from vast financial datasets. This is a full-time, self-employed contractor position offering flexibility with only 2 days per month required at our coworking space near Bologna Centrale station.

What You'll Do

• Prompt Engineering Excellence: Design, test, and optimize sophisticated prompts for our QUIN AI assistant to deliver accurate, contextual market insights and trading guidance
• LLM Optimization: Fine-tune and optimize large language models for financial domain expertise, ensuring QUIN provides reliable, compliant, and valuable trading insights
• Classical ML Implementation: Develop and deploy traditional machine learning models for pattern recognition, anomaly detection, and predictive analytics on financial time series
• Statistical Analysis: Apply rigorous statistical methods including hypothesis testing, A/B testing, and statistical significance validation to evaluate model performance and trading strategies
• Data Pipeline Development: Build robust data pipelines for processing real-time market data, including options flow, volatility metrics, and technical indicators
• Model Validation & Testing: Implement comprehensive testing frameworks for both LLM outputs and classical ML models, ensuring accuracy and reliability in production
• Prompt Library Management: Create and maintain a comprehensive library of prompts for different trading scenarios, market conditions, and user expertise levels
• Performance Monitoring: Develop metrics and monitoring systems to track prompt effectiveness, model accuracy, and user satisfaction with AI-generated insights
• Cross-functional Collaboration: Work closely with quantitative researchers, traders, and engineers to translate complex financial concepts into effective AI interactions
• Research & Innovation: Stay current with latest developments in prompt engineering, LLMs, and apply cutting-edge techniques to financial applications

What we are looking for:

Must-Have Skills:

• 1-3 years of experience in data science, machine learning, or related field (academic projects count for junior positions)
• Strong understanding of prompt engineering principles and best practices for LLMs
• Proficiency in Python for data science (pandas, numpy, scikit-learn, statsmodels)
• Solid foundation in statistics and hypothesis testing
• Experience with classical machine learning algorithms:
  • Supervised learning (regression, classification, ensemble methods)
  • Unsupervised learning (clustering, dimensionality reduction)
  • Time series analysis and forecasting
• Understanding of model evaluation metrics and validation techniques
• Experience with A/B testing and experimental design
• Basic understanding of natural language processing (NLP) concepts
• Familiarity with version control (Git) and collaborative development
• Strong analytical and problem-solving skills
• Excellent written and verbal communication skills in English
• Fluent Italian for local team coordination
• EU work authorization or ability to work as self-employed contractor in Italy

Nice-to-Have:

• Knowledge of financial markets and trading concepts
• Experience with financial modeling and quantitative analysis
• Understanding of options pricing, volatility modeling, or technical indicators
• Experience with prompt engineering for GPT-4, Claude, or other LLMs
• Familiarity with LangChain, LlamaIndex, or similar LLM frameworks
• Experience with RAG (Retrieval Augmented Generation) systems
• Knowledge of vector databases (Pinecone, Weaviate, ChromaDB)
• Experience with MLOps and model deployment pipelines
• Familiarity with deep learning frameworks (TensorFlow, PyTorch)
• Experience with real-time data processing and streaming analytics
• AWS ML services experience (SageMaker, Bedrock)
• Knowledge of financial regulations and compliance in AI systems
• Background in quantitative finance or econometrics

Technical Environment You'll Work With

AI/ML Stack:

• LLM Technologies: GPT-4, Claude, open-source models
• ML Libraries: scikit-learn, XGBoost, LightGBM, statsmodels
• Deep Learning: TensorFlow, PyTorch (as needed)
• NLP Tools: Transformers, spaCy, NLTK
• Prompt Engineering: LangChain, custom prompt frameworks
• Data Science: pandas, numpy, matplotlib, seaborn, plotly

Data & Infrastructure:

• Languages: Python (primary), SQL
• Databases: DynamoDB, RDS, ClickHouse
• Data Lake: S3, AWS Glue, Athena
• Streaming: Kinesis for real-time market data
• Compute: AWS Lambda, ECS for model deployment
• Version Control: Git, GitHub
• Collaboration: Jupyter notebooks, Google Colab

Financial Data:

• Real-time market data feeds
• Options flow and volatility metrics
• Technical indicators and trading signals
• Historical price and volume data
• Alternative data sources

What We Offer

• Competitive Compensation: Attractive contractor rates commensurate with experience
• AI Innovation: Work on cutting-edge LLM applications in finance
• Learning Opportunities:
  • Access to latest AI/ML tools and technologies
  • Learn from professional traders with 10-20+ years of experience
  • Free access to our 500+ hours of trading education content
  • Exposure to quantitative finance and algorithmic trading
• Career Growth: Clear progression path from junior to senior roles with mentorship from experienced data scientists
• Flexible Work: Remote-first approach with minimal office presence
• Minimal Office Requirement: Only 2 days per month at our Bologna coworking space
• Impact: Your work directly enhances QUIN, helping 15,000+ traders make better decisions
• International Team: Collaborate with professionals from London, Milan, Dubai, Switzerland, New York, and more
• Domain Expertise: Develop deep knowledge in financial markets while applying AI
• Innovation Culture: Freedom to experiment with latest prompt engineering techniques

Work Arrangement

• Employment Type: Self-employed contractor (Partita IVA preferred)
• Hours: Full-time, flexible schedule
• Location: Remote with 2 days per month at Bologna coworking space
• Team: Part of the AI/Data Science team, closely collaborating with quantitative researchers

How to Apply

Ready to shape the future of AI-powered trading assistance? We want to hear from you!

Please send your application including:

• Your CV highlighting data science and any prompt engineering experience
• Portfolio or GitHub showcasing ML projects (financial projects are a plus)
• Examples of prompt engineering work or LLM applications you've built
• Description of statistical analyses or A/B tests you've conducted
• Any experience with financial data or trading systems
• A brief explanation of your interest in combining AI with financial markets
• Your availability and rate expectations

MenthorQ is committed to building a diverse and inclusive team. We welcome applications from all qualified candidates regardless of background.

Key skills/competency

• Prompt Engineering
• Large Language Models (LLMs)
• Quantitative Modeling
• Financial Technology (FinTech)
• Python Data Science
• Machine Learning (ML)
• Statistical Analysis
• Data Pipelines
• Time Series Analysis
• Natural Language Processing (NLP)