Chief Data Scientist
India - Remote
Weekday
At Weekday, we help companies hire engineers who are vouched by other software engineers. We are enabling engineers to earn passive income by leveraging & monetizing the unused information in their head about the best people they have worked...This role is for one of the Weekday's clients
Salary range: Rs 3000000 - Rs 7000000 (ie INR 30-70 LPA)
Min Experience: 5 years
JobType: full-time
We are seeking an experienced Chief Data Scientist to lead the development of a cutting-edge, data-driven matchmaking algorithm that enables users to make deeply informed and meaningful relationship choices. By leveraging psychometric testing and AI technologies, you will play a pivotal role in transforming the matchmaking experience.
This role requires a seasoned leader with deep expertise in data science, machine learning, and psychometrics, and a strong track record of delivering AI-powered products. You will work closely with product, engineering, and growth teams to build a highly scalable, innovative platform.
Requirements
Key Responsibilities
Algorithm Development & Optimization
- Lead the design, development, and iterative improvement of the matchmaking algorithm based on data-driven insights.
- Identify and incorporate key data signals that influence successful matchmaking outcomes.
- Optimize the algorithm for scalability and performance without compromising accuracy or speed.
Data Analysis & Hypothesis Testing
- Develop and test hypotheses about factors driving successful matches, leveraging user behavior and engagement data.
- Design and execute A/B and multivariate tests to validate hypotheses and enhance algorithmic efficiency.
- Apply statistical models and machine learning techniques to uncover hidden patterns within large datasets.
Scalability & Performance at Scale
- Ensure the matchmaking algorithm can handle growing user bases with optimized performance.
- Collaborate with data engineering teams to build robust, real-time data pipelines.
- Implement distributed computing solutions to manage large-scale computations effectively.
Data Engineering & Infrastructure
- Partner with data engineering teams to create scalable data infrastructure supporting algorithmic needs.
- Oversee the development of real-time, high-quality data pipelines.
- Ensure efficient data storage, collection, and retrieval processes aligned with legal and compliance standards.
Performance Monitoring & Improvement
- Monitor key metrics like match success rates, user engagement, and retention to track algorithm performance.
- Analyze behavioral data to enhance the predictive accuracy and personalization of recommendations.
- Continuously iterate and refine the algorithm to improve user outcomes.
Machine Learning & AI Integration
- Integrate advanced machine learning models to boost matchmaking predictions, user segmentation, and personalization.
- Develop adaptive recommendation systems that evolve based on real-time user data.
- Leverage AI to automate decision-making processes and optimize the overall user experience.
Data Strategy & Governance
- Define and implement strategies for high-quality data collection to fuel machine learning and analytics efforts.
- Establish best practices for data governance, privacy, and security in compliance with regulations like GDPR and CCPA.
- Collaborate with legal and compliance teams to ensure ethical use of user data.
Key Qualifications
Data Science Expertise
- 6+ years of experience in data science, focusing on machine learning, algorithm development, and optimization.
- Strong background in working with large datasets and building recommendation or matchmaking systems.
- Expertise in predictive modeling, statistical analysis, and both supervised and unsupervised learning techniques.
Scalability & Performance Optimization
- Experience developing scalable algorithms for large, data-intensive applications.
- Knowledge of distributed computing, parallel processing, and real-time data architectures.
Technical Skills
- Proficient in Python, R, or similar programming languages and data science libraries (e.g., Pandas, TensorFlow, Scikit-learn).
- Skilled in A/B testing, experimentation frameworks, and data visualization tools like Tableau or Power BI.
- Familiarity with data engineering practices and large-scale data management.
Leadership & Collaboration
- Strong leadership skills, including experience building and managing data science teams.
- Ability to communicate complex technical ideas clearly to non-technical stakeholders.
- Proven experience collaborating across product, engineering, and business functions to drive results.
Business Acumen
- Ability to align data science initiatives with broader business goals.
- Prior experience or familiarity with matchmaking, recommendation engines, or consumer technology platforms is an advantage.
Desired Skill Set
- Deep understanding of psychometric testing and its integration into machine learning models.
- Expertise in AI, machine learning, natural language processing (NLP), and behavior prediction algorithms.
- Experience with big data technologies like Hadoop, Spark, and cloud platforms such as AWS, Google Cloud, or Azure.
- Strong knowledge of security protocols and fraud detection practices.
- Strategic thinking and the ability to translate complex data into actionable business insights.
Education & Experience
- Bachelor’s, Master’s, or Ph.D. in Data Science, Statistics, Computer Science, Applied Mathematics, or a related field from a reputed institution (Ivy League preferred).
- 7+ years of experience in data science and machine learning roles, with at least 3 years in a leadership position.
- Proven success in developing and deploying AI-driven products, preferably in the matchmaking, dating, or consumer technology industries.
- Experience with psychometric analysis, behavioral modeling, and personalized AI-driven user experiences.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: A/B testing Architecture AWS Azure Big Data Computer Science Data analysis Data governance Data management Data pipelines Data strategy Data visualization Engineering GCP Google Cloud Hadoop Machine Learning Mathematics ML models NLP Pandas Pipelines Power BI Predictive modeling Privacy Python R Scikit-learn Security Spark Statistics Tableau TensorFlow Testing Unsupervised Learning
Perks/benefits: Career development
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