Data Scientist

Chief Data Scientist
Job Description

Who we are

Verona is an authenticated matchmaking community designed exclusively for the modern global Indian. Were on a mission to foster fulfilling partnerships that last lifetimes. In a country where dating app disillusionment abounds Verona makes the matchmaking process delightfuland 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 (exFortress Investment Group CEO of Galaxy Group Investments) & Mr. Rishi Jaitly (exTwitter CEO for Asia Middle East and Africa).

Security and Science make the basis of Verona where before matchmaking there will be Fintechgrade 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 individuals core & shared values.

The 3 Ss of Verona Standards Security and Science

Role Overview
As the Chief Data Scientist you will lead the charge in revolutionizing how matchmaking works by building and refining a sophisticated datadriven algorithm that identifies and recommends the most compatible life partners for our users. Leveraging psychometric tests and AI this role is central to Veronas 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 AIdriven products. You will collaborate closely with product engineering and growth teams to implement a highly scalable and innovative matchmaking platform.

Key Responsibilities

1. Algorithm Development & Optimization

• Lead the design development and continuous refinement of Veronas matchmaking algorithm ensuring it evolves based on datadriven 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 Veronas 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 realtime data processing and largescale 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 algorithms performance at scale addressing any bottlenecks or inefficiencies that arise as the platform grows.

4. Data Engineering & Infrastructure

• Work closely with the data engineering team to design and implement the necessary data infrastructure to support largescale data processing for the algorithm.
• Oversee the development of scalable data pipelines to ensure the algorithm has access to highquality realtime data.
• Ensure data collection storage and retrieval processes are efficient and meet the needs of both algorithmic performance and legal compliance.

5. 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 algorithms predictive accuracy.
• Continuously iterate on the matchmaking algorithm to improve its precision relevance and scalability over time.

6. Machine Learning & AI Integration

• Implement advanced machine learning models to improve matchmaking predictions user segmentation and personalized recommendations.
• Develop recommendation engines and models that learn and adapt based on realtime user data improving the overall matchmaking process.
• Leverage AI techniques to automate decisionmaking processes and further optimize the user experience.

8. Data Strategy & Governance

• Define and implement data collection strategies that ensure the availability of highquality 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).
• Work with legal and compliance teams to ensure ethical use of data in algorithm development and matchmaking practices.

Key Qualifications:

Data Science Expertise6 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 OptimizationExperience building and optimizing algorithms for largescale applications ensuring scalability and high performance under increasing data volumes.Strong understanding of distributed computing and parallel processing to enhance algorithm performance.Proficiency in designing and working with largescale data architectures and realtime data processing pipelines.Technical SkillsProficient in programming languages such as Python R or similar with experience in data science libraries and tools (e.g. Pandas TensorFlow Scikitlearn).Experience with A/B testing experimentation frameworks and hypothesisdriven data analysis.Handson experience with data visualization tools (e.g. Tableau Power BI) and data engineering practices.Leadership & CollaborationStrong leadership skills with experience building and leading data science teams.Excellent communication skills with the ability to present technical findings to nontechnical stakeholders in a clear and actionable manner.Proven experience collaborating with product managers engineers and business leaders to achieve crossfunctional goals.Business AcumenAbility to align data science initiatives with business objectives driving tangible outcomes that contribute to Veronas overall growth.Strong understanding of the matchmaking or recommendation domain is a plus but not mandatory.

Skill Set

• Deep understanding of psychometric testing and its application in machine learning models.
• Proficiency in AI machine learning and natural language processing (NLP) to develop sophisticated recommendation systems.
• Strong background in predictive modeling including expertise in algorithms related to behavior prediction collaborative filtering and classification.
• Experience with big data technologies such as Hadoop Spark or similar platforms to manage largescale datasets.
• Proficiency in programming languages like Python R SQL and experience with data visualization tools (e.g. Tableau PowerBI).
• Expertise in cloud computing environments such as AWS Google Cloud or Azure for data storage processing and model deployment.
• Knowledge of fraud detection and security protocols to ensure user data safety.
• A strategic mindset with the ability to transform complex data into actionable business insights presenting them clearly to nontechnical stakeholders.

Education and Experience

• Bachelors Masters 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 AIdriven products preferably in the matchmaking dating or consumer technology space.
• Experience with psychometric analysis behavior modeling and AIdriven user personalization.

Location
New York USA or Gurugram Haryana INDIA

For further clarifications please reach out to

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