Data Science and Machine Learning Senior Associate

KYYBA, Inc

United States

United States

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À propos

Employees in this job function are responsible for predicting and/ or extracting meaningful trends/ patterns/ recommendations from raw data, leveraging data science methodologies including Machine Learning (ML), predictive modeling, math, statistics, advanced analytics, etc. Key Responsibilities: 1) Understand business requirements and analyze datasets to determine suitable approaches to meet analytic business needs and support data-driven decision-making 2) Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data driven decision-making 3) Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends 4) Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation 5) Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness Desired Experience (Minimum Requirements) " Professional Experience: 5+ years of experience in a Data Science role, with a proven track record of delivering models that impact business outcomes. " Programming Expertise: Expert proficiency in Python (specifically libraries like Pandas, NumPy, Scikit-learn, SciPy) or R. " Machine Learning Foundations: Deep understanding of a broad range of ML techniques, including Gradient Boosting (XGBoost/LightGBM), Random Forests, GLMs, and Clustering. " Advanced SQL: Ability to manipulate and extract data from complex, multi-terabyte distributed databases. " Mathematics & Statistics: Strong foundation in linear algebra, calculus, and advanced statistical inference. " Software Best Practices: Experience with version control (Git) and writing clean, modular, and maintainable code.
Skills Required: ALGORITHMS, Python, GCP
Skills Preferred: N/A
Experience Required: Senior Associate Exp: 3 to 5 years experience in relevant field
Experience Preferred: Hybrid 4 days a week onsite
Education Required: Master's Degree
Additional Information: " Model Development: Design, develop, and deploy high-performance machine learning models (supervised, unsupervised, and reinforcement learning) to address business needs such as churn prediction, recommendation engines, or demand forecasting. " Experimental Design: Lead the design and analysis of large-scale experiments (A/B testing, multivariate testing) to validate hypotheses and measure the impact of product changes. " Feature Engineering: Architect and implement robust data pipelines and feature engineering processes to improve model accuracy and scalability. " Algorithm Optimization: Evaluate and refine existing algorithms to improve computational efficiency and predictive power. " Stakeholder Influence: Act as a strategic advisor to leadership, translating complex algorithmic outcomes into business-centric narratives that drive ROI. " Technical Leadership: Mentor junior data scientists and contribute to the team s internal library of best practices, code standards, and research methodologies. " Collaboration with Engineering: Partner with ML Engineers and DevOps to integrate models into production systems, ensuring reliability and monitoring model drift.

United States

Compétences linguistiques

English

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