We're seeking to collaborate with motivated, independent PhD graduates or doctoral students on approximately seven new projects in 2024. If you’re interested in contributing to cutting-edge investment insights and data analysis, please get in touch! This could be in colaboration with a university or as independent study.

Sov.ai is at the forefront of integrating advanced machine learning techniques with financial data analysis to revolutionize investment strategies. We are working with three of the top 10 quantitative hedge funds, and with many mid-sized and boutique firms.

Our platform leverages diverse data sources and innovative algorithms to deliver actionable insights that drive smarter investment decisions.

By joining Sov.ai, you'll be part of a dynamic research team dedicated to pushing the boundaries of what's possible in finance through technology. Before expressing your interest, please be aware that the research will be predominantly challenging and experimental in nature.

We offer a wide range of projects that cater to various interests and expertise within machine learning and finance. Some of the exciting recent projects include:

• Predictive Modeling with GitHub Logs: Develop models to predict market trends and investment opportunities using GitHub activity and developer data.
• Satallite Data Analysis: Explore non-traditional data sources such as social media sentiment, satellite imagery, or web traffic to enhance financial forecasting.
• Data Imputation Techniques: Investigate new methods for handling missing or incomplete data to improve the robustness and accuracy of our models.

Please visit docs.sov.ai for more information on public projects that have made it into the subscription product. If you already have a corporate sponsor, we are also happy to work with them.

• Innovative Environment: Engage with the latest technologies and methodologies in machine learning and finance.
• Collaborative Team: Work alongside a team of experts passionate about driving innovation in investment insights.
• Flexible Projects: Tailor your research to align with your interests and expertise, with the freedom to explore new ideas.
• Experienced Researchers: Experts previously from NYU, Columbia, Oxford-Man Institute, Alan Turing Institute, and Cambridge.
• Post Research: Connect with alumni that has moved on to DRW, Citadel Securities, Virtu Financial, Akuna Capital, HRT.

If you’re excited about leveraging your expertise in machine learning and finance to drive impactful research and projects, we’d love to hear from you! Please reach out to us at research@sov.ai with your resume and a brief description of your research interests.

Join us in shaping the future of investment insights and making a meaningful impact in the world of finance!

A series looking at implementing python solutions to solve practical business problems. Share your own projects on this subreddit, r/datascienceproject. Every week we will look at hand picked businenss solutions. See the following google drive for all the code and github for all the data. If you follow the LinkedIn page, you would be able to see the lastest developments.

Outlier Analysis, Model Selection, Missing Values, Descriptive Statistics

Process Text, pyLDAvis, Word Embeddings, Text Evaluation, fuzzywuzzy

RFM Analysis, Pareto Model, NDB Model, Gamma-Gamma Model, CLV Model, Constraint Programming

Radar, Silhouette, PCA, Grouping, Invoices, Inventory, Datatable, Basket,

Week, EDA, Simulated, Prediction, Dummy Variable

Neural Network, Sales, Relu, LSTM, CNN, Evaluation

Full Pipeline, Random Forest, Visualisation, Grid Search, Confidence Interval

Efficient Frontier, Stocks, Modern Portfolio Theory, Pivot, Simulations, Minimum Volatility, Sharpe Ratio

GDP, Life Satisfaction, Linear Regression Plots, Prediction Model

Default, Credit Scores, Visualisations, Data Cleaning, ROC Curves, Multi-class Classification

Capital Allocation, Decision Trees, Acquisitions, Investment

Voting Classifiers, Bagging Ensembles, SMOTE, XGBoost, Cross-validation

OSEMN, Bagging Ensembles, AUC, Model Comparison, ROC Graph, Feature Importance Graph