Data Scientist II - Machine Learning Models

Role Overview:As a Data Scientist II, you will serve as a pivotal bridge between complex data architecture and actionable business strategy. Your day-to-day involves architecting sophisticated machine learning models, conducting deep-dive exploratory data analysis, and translating technical findings into high-impact narratives for senior leadership. You will collaborate closely with cross-functional product, engineering, and marketing teams to identify growth levers and optimize user experiences. By transforming raw, unstructured data into predictive insights, you will directly influence product roadmaps and drive measurable improvements in customer retention and operational efficiency.Key Responsibilities :- Develop and deploy scalable machine learning models to solve high-priority business challenges, ensuring seamless integration into production environments.- Partner with product managers to define key performance indicators and design rigorous A/B testing frameworks that validate feature efficacy.- Translate complex analytical outputs into intuitive visualizations and strategic recommendations for non-technical stakeholders to facilitate data-driven decision-making.- Optimize existing data pipelines and feature engineering processes to enhance model accuracy and reduce latency in real-time applications.- Mentor junior team members by conducting code reviews and promoting best practices in statistical modeling and reproducible research.Required Skillset :- Demonstrated proficiency in Python or R for statistical modeling, coupled with advanced SQL skills for extracting and manipulating large-scale datasets.- Proven ability to communicate technical concepts to diverse audiences, ensuring that analytical insights are clearly understood and actionable.- Strong foundation in machine learning algorithms, including regression, classification, clustering, and deep learning frameworks, supported by a degree in Computer Science, Statistics, Mathematics, or a related quantitative field.- Exceptional problem-solving aptitude with a track record of managing end-to-end data projects in a fast-paced, collaborative environment.- High degree of adaptability to hybrid work structures, maintaining productivity and team cohesion through effective digital collaboration tools. (ref:hirist.tech)