SINA RAJAEEIAN

Covering AI applications, deep learning, probabilistic modeling, reinforcement learning, optimization, LLMs, and large-scale data analysis with extensive hands-on projects using real-world datasets and related to businesses.

Bachelor Thesis: ML-Driven Predictive Maintenance for Mining. Developed a machine-learning prototype for Epiroc AB using historical sensor data to predict equipment condition. Demonstrated potential to cut unplanned downtime by 20-30%, enabling significant operational cost savings.

Applied economic models to real-life financial decisions including saving, investing, and housing.

• Represent a first-round deep and industrial tech fund (>50 portfolio companies) within the Stockholm startup ecosystem
• Source and evaluate founders in AI/ML and deep tech; conduct market, product, and team assessments
• Build pipeline by facilitating introductions between founders and the investment team

• Identified an ideal M&A target for a client by assessing strategic and financial criteria
• Constructed a DCF model to calculate the valuation of the M&A target
• Adjusted the valuation model to account for a competitor bid and supply chain interruption
• Created a company profile and summary of an auction process for the client

• Analyzed and visualized datasets using Excel and Tableau to support data-driven decision-making

• Identified and engaged potential sponsors to foster partnerships for project funding and equipment supply

• Managed the student association's savings portfolio as part of a student-driven investment fund team
• Conducted investment analysis, equity research, and valuation

• Specialized in selling electricity contracts through direct customer engagement
• Achieved recognition as the month's top seller

• Successfully sold electricity contracts in a competitive direct sales environment
• Consistently exceeded sales targets and contributed to team goals

• Built and launched a minimalist AI powered productivity for students and professionals
• Responsible for product design, implementation, SEO, marketing, analytics and deployment end-to-end
• Reached 100 active users to date through organic adoption and word of mouth

• IT consulting company that helps clients further develop their no-code generated app and websites
• Take products that are 80% completed by no-code tools like Lovable and develop them into fully launch-ready solutions

• Built FX Sentinel, an AI tool for CFO risk analysis, collaborating with a newly formed team under 3 hours

• Awarded first place for the technical solution among competing teams
• Developed a solution for document comparison to analyze differences between versions of legal documents

• Recognized for exceptional ability in solving quantitative finance problems with a focus on optimization and trading

Machine Learning:

• Models: Deep Learning (CNNs, RNNs), Reinforcement Learning, Probabilistic Models, Transformers, Support Vector Machines (SVMs)
• Theory: Statistical Learning Theory, Bayesian Methods, Optimization for ML
• Applications: Natural Language Processing (NLP), Computer Vision, Predictive Modelling
• Frameworks: PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy

AI Tools: Lovable, Claude CLI, Generative AI for Marketing, prompt design

Programming: Python, Java, Swift, Swift UI, Docker

Web Development: JavaScript, React.js, Node.js, Vue.js, Express, CSS, PostgreSQL, MongoDB

Web Design: Tailwind CSS, Figma, Bootstrap, Canvas, Photoshop

Cloud & Data: Google Cloud Platform, Tableau, Statistical Analysis, Data Cleansing

Finance & Accounting: Financial Analysis, DCF Evaluation, Capital Structure, Budgeting, Fortnox

MS Office: Excel, PowerPoint