Associate Software Engineer - Data Science

Devsinc is on the lookout for Data Scientists with 6 months - 1.5 years of experience, with a particular focus on machine learning (ML). This role is ideal for individuals who have begun to develop their skills in ML methodologies and are eager to apply this knowledge to solve real-world challenges. The successful candidate will demonstrate a strong foundation in ML techniques, an analytical mindset for unraveling complex data puzzles, and a dedication to contributing to data-driven decisions and innovations.

Responsibilities :

• Design, develop, and deploy machine learning models to address specific business challenges. This includes data preprocessing, feature engineering, model selection, training, and validation.
• Perform exploratory data analysis to uncover hidden patterns, correlations, and insights within structured and unstructured data. Utilize these findings to refine ML models and approaches.
• Engage with a multidisciplinary team of data scientists, engineers, and business stakeholders to refine data requirements and deliver ML-driven solutions.
• Create clear visualizations to represent the outcomes of ML models and analyses. Prepare comprehensive reports and presentations that translate complex ML concepts and findings into actionable business insights.
• Actively pursue learning opportunities in advanced machine learning techniques and algorithms. Incorporate cutting-edge research and tools into projects to enhance model performance and efficiency.
• Assist in the development of prototypes for predictive models and other ML applications, testing their effectiveness in real-world scenarios.
• Cross-Functional Application of Insights, Datasets, Code, and Models : Look for opportunities to use insights / datasets / code / models across other functions in the organization (for example in the HR and marketing departments)
• Stay curious and enthusiastic about using algorithms to solve problems and enthuse others to see the benefit of your work.
• Maintain clear and coherent communication, both verbal and written, to understand data needs and report results.

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