Hire the Top 3% of Data Miners in 48 Hours

Miguel has a Ph.D. in computer science and bioinformatics. He learned about data mining 20 years ago and discovered a great field of application in Bioinformatics. Since then, Miguel has worked in many areas focusing on cancer genomics (text-mining, systems biology, web development, and workflows). He has written hundreds of thousands of lines of open-source code and developed the Rbbt framework, one of the most effective tools for data analysis, downloaded over a million times.

Miguel's background in software engineering and his PhD in Data Mining from Carnegie Mellon University make him the perfect bridge between product managers, developers, and data scientists. He has started and managed teams of data scientists and engineers to build data pipelines and machine learning infrastructure to support Insights and live ML deployments. He holds multiple best paper awards, and his transfer of research work to product features in fintech and ad tech has led to four patents.

François is a seasoned leader with experience building data platforms and machine learning solutions at major technology companies and startups in B2C and B2B settings. François has spent seven years at Spotify building data infrastructure teams as a manager and leveraging machine learning techniques to improve the music catalog as a staff engineer.

Merve is an experienced machine learning engineer who takes pleasure in revealing the story of data and building predictive models. She has a proven track record of designing and implementing pipelines for extracting, validating, cleaning, transforming, and modeling data. Merve is passionate about solving real-world industry problems and is eager to take on new challenges and opportunities.

Final-year PhD candidate in the Department of Electrical and Electronic Engineering at the University of Melbourne. My research defines CRISPR-Cas targeting principles by applying machine learning and molecular modeling, with applications in RNA-targeting therapeutics.

Brian is an experienced data scientist and machine learning engineer with a track record of researching and deploying a wide range of models for natural language processing (NLP) tasks, computer vision algorithms, classical machine learning algorithms, Bayesian statistical models, time series analysis models, and large-scale data mining algorithms.

Adrian is a software engineer and data scientist working at the intersection of software engineering, computer vision, and machine learning. He has experience in research, multinational corporations, and startup environments and was awarded patents for real estate and financial modeling projects. Adrian also contributed to surveillance systems for Singapore's border security and participated in competitive coding and hackathons, consistently ranking in the top 1% on platforms like HackerRank.

Amanbir has 10 years of experience in data science, analytics, and back-end engineering. He has worked at a large multilateral organization and with early-stage tech startups. Amanbir excels at working with clients in tackling complex business problems and has deep expertise in machine learning, data analysis, and building scalable web apps.

George is a results-driven data scientist who brings a diverse skill set and rich experience to the table. His expertise lies in data translation and a versatile toolkit, including Python, SQL, and machine learning. George excels at distilling complex findings into actionable and comprehensible insights, making data accessible and impactful for stakeholders.

Darin is a data scientist and engineer with a PhD in physics from Stanford. He's passionate about data and machine learning and has worked on data science projects across numerous industries and applications. Darin's co-founded an AI company and led a team of data scientists to build a product that uses machine learning and optimization techniques to reduce energy consumption in data centers. He's eagerly waiting for quantum computers.