Description Collaborate with cross-functional teams to identify business challenges and develop data-driven solutions that drive business impact. Collect, clean, and preprocess large volumes of structured and unstructured data from diverse sources to prepare it for analysis. Design and implement machine learning algorithms and statistical models to extract insights, predict outcomes, and optimize business processes. Develop and deploy predictive models, recommendation engines, and other data-driven applications to enhance customer experiences and drive revenue growth. Perform exploratory data analysis to uncover patterns, trends, and anomalies in data and generate hypotheses for further investigation. Communicate findings and insights to stakeholders through clear and compelling data visualizations, reports, and presentations. Continuously monitor and evaluate model performance, making iterative improvements and optimizations as needed. Stay abreast of the latest developments in data science, machine learning, and artificial intelligence, and proactively identify opportunities to apply emerging techniques and technologies to solve business problems. Collaborate with data engineers and IT teams to ensure data quality, integrity, and security throughout the data lifecycle. Mentor and provide guidance to entry level team members, fostering a culture of learning, collaboration, and innovation. Requirements

Master's or Ph.D. degree in Computer Science, Statistics, Mathematics, or a related quantitative field. Minimum of 4 years of experience in data science or related roles, with a proven track record of applying advanced analytical techniques to solve complex business problems. Proficiency in programming languages such as Python, R, or Scala, as well as experience with data manipulation and analysis libraries (e.g., pandas, NumPy, scikit-learn). Strong understanding of machine learning algorithms and statistical modeling techniques, including supervised and unsupervised learning, regression, classification, clustering, and time series analysis. Hands-on experience with machine learning frameworks and platforms such as TensorFlow, PyTorch, or Apache Spark. Proficiency in SQL and experience working with relational databases and big data technologies (e.g., Hadoop, Spark, SQL Server, PostgreSQL). Excellent problem-solving skills with a keen attention to detail and a drive for continuous improvement. Strong communication and interpersonal skills, with the ability to effectively collaborate with cross-functional teams and communicate technical concepts to non-technical stakeholders. Experience working in Agile environments and familiarity with DevOps practices is a plus. Knowledge of cloud platforms such as AWS, Azure, or Google Cloud Platform is beneficial but not required.

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