Data Scientist Gurugram, Haryana, India - TBWA\Chiat\Day : Job Details

Verona is an authenticated matchmaking community designed exclusively for the modern, global Indian. We're on a mission to foster fulfilling partnerships that last lifetimes. In a country where dating app disillusionment abounds, Verona makes the matchmaking process delightful—and effective.Verona was founded by two serial entrepreneurs Mr. Poshak Agrawal and Mr. Rahul Subramaniam, and backed by some of the biggest names in global technology, such as Mr. Michael Novogratz (ex-Fortress Investment Group, CEO of Galaxy Group Investments) & Mr. Rishi Jaitly (ex-Twitter CEO for Asia, Middle East, and Africa).Security and Science make the basis of Verona where before matchmaking there will be Fintech-grade authentication to ensure fraud detection. Through a deep appreciation for the science of compatibility, with Verona, we will be using AI for matchmaking based on an individual's core & shared values.The 3 S's of Verona - Standards, Security, and ScienceRole OverviewAs the Chief Data Scientist, you will lead the charge in revolutionizing how matchmaking works by building and refining a sophisticated, data-driven algorithm that identifies and recommends the most compatible life partners for our users. Leveraging psychometric tests and AI, this role is central to Verona's mission of enabling users to make deeply informed and meaningful relationship choices.This role demands a seasoned leader with deep expertise in data science, machine learning, and psychometrics, combined with a proven track record of successfully delivering AI-driven products. You will collaborate closely with product, engineering, and growth teams to implement a highly scalable and innovative matchmaking platform.Key Responsibilities1. Algorithm Development & Optimization Lead the design, development, and continuous refinement of Verona's matchmaking algorithm, ensuring it evolves based on data-driven insights and testing.Identify key factors and data signals that influence successful matchmaking outcomes and incorporate them into the algorithm.Ensure that the algorithm is optimized for scalability and performance, handling increasing user activity without compromising accuracy or speed.2. Data Analysis & Hypothesis Testing Develop and test hypotheses about what drives successful matches, experimenting with different variables (e.g., user preferences, behavior, engagement metrics).Design and execute A/B tests, multivariate tests, and other experiments on live data to validate hypotheses and improve algorithmic efficiency.Use statistical models and machine learning techniques to uncover hidden patterns and insights in large datasets.3. Scalability & Performance at Scale Ensure that the matchmaking algorithm can scale to support Verona's growing user base, designing for performance optimization under increasing data loads.Collaborate with data engineering teams to build robust data pipelines, ensuring the algorithm can handle real-time data processing and large-scale computations efficiently.Implement distributed computing solutions where necessary to improve algorithm speed and responsiveness, especially as the user base expands globally.Monitor and optimize the algorithm's performance at scale, addressing any bottlenecks or inefficiencies that arise as the platform grows.4. Performance Monitoring & Improvement Track and measure the performance of the matchmaking algorithm using key metrics such as match success rates, user engagement, and retention.Analyze user behavior data to identify opportunities for improving match recommendations and refining the algorithm's predictive accuracy.Continuously iterate on the matchmaking algorithm to improve its precision, relevance, and scalability over time.Implement advanced machine learning models to improve matchmaking predictions, user segmentation, and personalized recommendations.Leverage AI techniques to automate decision-making processes and further optimize the user experience.Define and implement data collection strategies that ensure the availability of high-quality data for algorithm training and analysis.Establish best practices for data governance, privacy, and security, ensuring compliance with data protection regulations (e.g., GDPR, CCPA).Key Qualifications: Data Science Expertise: 6+ years of experience in data science, with a focus on machine learning, algorithm development, and optimization. Proven track record of working with large datasets and developing recommendation or matchmaking systems. Strong expertise in statistical analysis, predictive modeling, and machine learning algorithms (supervised and unsupervised).Scalability & Performance Optimization: Experience building and optimizing algorithms for large-scale applications, ensuring scalability and high performance under increasing data volumes. Strong understanding of distributed computing and parallel processing to enhance algorithm performance.Technical Skills: Proficient in programming languages such as Python, R, or similar, with experience in data science libraries and tools (e.g., Pandas, TensorFlow, Scikit-learn). Experience with A/B testing, experimentation frameworks, and hypothesis-driven data analysis.Leadership & Collaboration: Strong leadership skills with experience building and leading data science teams. Excellent communication skills, with the ability to present technical findings to non-technical stakeholders in a clear and actionable manner.Business Acumen: Ability to align data science initiatives with business objectives, driving tangible outcomes that contribute to Verona's overall growth. Strong understanding of the matchmaking or recommendation domain is a plus, but not mandatory.Education and Experience:Bachelor's, Master's, or Ph.D. from an Ivy League institution in a field such as Data Science, Statistics, Computer Science, or Applied Mathematics.7+ years of experience in data science or machine learning roles, with at least 3 years in a leadership capacity.Proven track record of developing and deploying AI-driven products, preferably in the matchmaking, dating, or consumer technology space.Experience with psychometric analysis, behavior modeling, and AI-driven user personalization.#J-18808-Ljbffr