AI/ML – Data Scientist Intern

Time: 12 weeks | ~10–15 hours/weekCompensation: UnpaidFunction: Artificial Intelligence, Machine Learning, Data Science, Financial Technology About UsLXMQ is an AI-powered financial intelligence platform that helps consumers make smarter personal-finance decisions. Our work combines machine learning, data science, recommendation systems, financial data modeling, and responsible AI to support personalized insights for credit cards, spending, rewards, debt, fees, and credit health. For more information visit www.lxmq.aiRole OverviewThe Product Engineering team is looking for “AI/ML – Data Scientist Interns”, who will help build and evaluate data pipelines, synthetic datasets, feature engineering workflows, recommendation models, predictive models, and internal AI prototypes.This internship is ideal for students or early-career candidates who want hands-on experience working on real-world AI/ML problems in fintech industry. ResponsibilitiesAs an AI/ML – Data Scientist Intern, you may work on:Building, cleaning, and analyzing structured financial datasets.Creating synthetic financial data for internal model testing and experimentation.Developing feature engineering pipelines for transactions, account activity, merchant categories, balances, payments, rewards, fees, and user behavior.Prototyping machine learning models for recommendations, ranking, forecasting, risk signals, and user decision support.Experimenting with graph-based features, embeddings, or relationship-based modeling.Evaluating model performance using accuracy, calibration, robustness, and business-impact metrics.Comparing model outputs against simple baseline approaches.Helping create notebooks, dashboards, model evaluation reports, and internal documentation.Supporting responsible AI work, including privacy-aware testing, fairness checks, explainability, and guardrail validation.Collaborating with product and engineering teams to turn AI ideas into working prototypes. Required QualificationsCurrently pursuing or recently completed a MS/PhD degree in Computer Science, Data Science, AI/ML, Statistics, Mathematics, Engineering or a related field.Strong Python programming skills.Experience with pandas, NumPy, scikit-learn, Jupyter notebooks, or similar tools.Strong understanding of machine learning concepts such as classification, regression, clustering, ranking, recommendations, or forecasting.Ability to work with structured datasets and document work clearly.Interest and/or exposure in fintech, personal finance, credit cards, consumer financial behavior, or applied AI. Preferred QualificationsExperience with PyTorch, TensorFlow, XGBoost, LightGBM, or similar ML frameworks.Familiarity with graph analytics, graph embeddings, NetworkX, PyTorch Geometric, Graph Neural Networks, or relationship-based modeling.Experience with recommendation systems, ranking models, contextual decisioning, or personalization.Exposure to synthetic data generation, simulation, time-series data, or event-driven datasets.Familiarity with model evaluation, calibration, A/B testing, causal inference, or offline policy evaluation.Interest in responsible AI, privacy-preserving ML, fairness testing, explainability, or model governance.Experience with SQL, Git, FastAPI, Streamlit, or cloud-based data tools. What Interns Will GainHands-on experience building applied AI/ML prototypes for a fintech product.Exposure to financial data modeling, recommendation systems, and responsible AI practices.Experience working with synthetic and structured financial datasets.Mentorship from LXMQ’s Product Engineering Head.Opportunity to contribute to models and tools that may influence LXMQ’s core platform.Certificate of CompletionLetter of Recommendation (for exceptional performance)LinkedIn Testimonial (for strong outcomes)Real-world portfolio experience (growth experiments + analytics)Priority consideration for future paid roles (no guarantee of employment) Expected DeliverablesBy the end of the internship, the intern should complete at least three meaningful deliverables, such as:A working ML prototype.A synthetic data generator or data-quality notebook.A feature engineering pipeline.A recommendation or ranking model prototype.A model evaluation dashboard.A documented experiment report.A model card or responsible-AI review.A cleaned and reusable internal research notebook. Application MaterialsCandidates should submit:Resume.GitHub, portfolio, research project, or sample notebook, if available.Short note explaining interest in AI/ML, fintech, and LXMQ.