Data Scientist/Machine Learning

About the position The Data Scientist - Machine Learning at The Friedkin Group (TFG) will design, develop, implement, maintain, and improve advanced data science initiatives across business units, directly aligning with strategic objectives. This role encompasses transforming innovative ideas into real-world solutions through the application of sophisticated analytical techniques such as machine learning, optimization, and advanced analysis. The incumbent will deliver impactful analytical solutions, ensuring these innovations are seamlessly embedded into business operations to drive decision-making, enhance operational efficiency, and foster a culture of continuous improvement and innovation. As part of this role, the applicant will play a significant part in setting the AI & ML agenda for The Friedkin Group, including working with business units to define potential opportunities, and defining standards and best practices for AI & ML at TFG. The essential functions of this position include designing, training, and implementing machine learning algorithms, both supervised and unsupervised. The Data Scientist will build, deploy, and maintain machine learning algorithms, interface endpoints, and back-end data infrastructure for digital products. They will perform data mining, exploration, and time series analysis, and design machine learning and advanced analytics solutions, algorithms, and cloud architectures needed to satisfy product features and functionality defined by product owners and other stakeholders in a production environment. The role requires working in all phases of the software development life cycle, including functional analysis, development of technical requirements, technical design, prototyping, coding, testing, deployment, data migration, and support. Additionally, the Data Scientist will assist in integrating subsystems such as data pipelines, AI/ML algorithms, and API interfaces into end-user facing products. Participation in daily scrums, working with the Scrum Master and QA Team on projects, and supporting delivery timelines and priorities are also key responsibilities. The role involves organizing and prioritizing individual workload with the scrum team through story pointing, creating detailed documentation that describes methodology, relevant instructions, and test results, and finding, analyzing, and fixing bugs and performance problems whenever and wherever they may occur. Responsibilities • Design, train, and implement machine learning (supervised and unsupervised) algorithms. , • Build, deploy, and maintain machine learning algorithms, interface endpoints, and back-end data infrastructure for digital products. , • Perform data mining, exploration, and time series analysis. , • Design machine learning and advanced analytics solutions, algorithms, and cloud architectures needed to satisfy product features and functionality defined by product owner and other stakeholders in a production environment. , • Work in all phases of the software development life cycle including functional analysis, development of technical requirements, technical design, prototyping, coding, testing, deployment, data migration, and support. , • Assist in integrating subsystems such as data pipelines, AI/ML algorithms, API interfaces into end-user facing products. , • Participate in daily scrums, work with Scrum Master and QA Team on projects, and support delivery timelines and priorities. , • Organize and prioritize individual workload with scrum team through story pointing. , • Create detailed documentation which describes methodology, relevant instructions, and test results. , • Find, analyze, and fix bugs and performance problems whenever and wherever they may occur. Requirements • Bachelor's degree in a related discipline and 4 years' experience in a related field, or a master's degree and 2 years' experience, or a Ph.D. and up to 1 year of experience, or 8 years' experience in a related field. , • Proficient in at least one analytical programming language relevant for data science, preferably Python ecosystem, with familiarity in R, machine learning libraries & frameworks (e.g., TensorFlow, PyTorch, scikit-learn), and data processing and visualization tools (e.g., SQL, Tableau, Power BI). , • Expertise in advanced analytical techniques (e.g., descriptive statistics, machine learning, optimization, pattern recognition, cluster analysis, etc.). , • Experience with Time-Series Forecasting Methods, Regression Models, Clustering/Dimensionality Reduction. , • Familiarity with Mixed-Integer-Linear-Programming Algorithms, CPLEX, Gurobi. , • Experience with cloud computing environments (AWS, Azure, or GCP) and Data/ML platforms (Databricks, Spark). , • Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, monitoring, and feedback loop. , • Experience in Supervised and Unsupervised Machine Learning including classification, forecasting, anomaly detection, pattern recognition using a variety of techniques such as decision trees, regressions, ensemble methods, and boosting algorithms. , • Good understanding of programming best practices, building for re-use and highly automated CI/CD pipelines. Nice-to-haves • Relevant certifications such as Microsoft Certified: Azure Data Scientist Associate or AWS Certified Machine Learning. Benefits • Medical insurance , • Dental insurance , • Vision insurance , • Wellness programs , • Retirement plans , • Paid leave , • Performance-based rewards , • Competitive compensation structure. Apply Job!