Data Science - Associate Software Engineer | Software Engineer

Overview

Devsinc is seeking motivated

Data Scientists

with

1–3 years of experience , particularly in

Artificial Intelligence (AI)

or

Machine Learning (ML) . This role is ideal for individuals who have built a strong foundation in ML methodologies and are eager to apply their skills to real-world business challenges. You will work closely with cross-functional teams to design, deploy, and optimize ML-driven solutions that support data-driven decision-making and innovation. Key Responsibilities

Model Development : Design, develop, and deploy ML models for business use cases, including data preprocessing, feature engineering, model training, evaluation, and deployment. Data Exploration : Conduct exploratory data analysis (EDA) to uncover patterns, correlations, and insights that inform model refinement and business strategies. Collaboration : Work with data scientists, engineers, and business stakeholders to translate business needs into ML-driven solutions. Visualization & Reporting : Build clear, compelling visualizations and reports to communicate ML outcomes and insights to both technical and non-technical audiences. Continuous Learning : Stay updated with the latest advancements in ML algorithms, tools, and best practices, and incorporate them into projects where applicable. Prototyping : Develop and test prototypes for predictive and analytical models in real-world scenarios. Communication : Maintain clear, structured communication to articulate data needs, methodologies, and outcomes effectively. Cross-Functional Impact : Identify opportunities to reuse datasets, code, or models across multiple business areas. Qualifications

Education : Bachelor’s degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or a related field (with significant ML coursework / projects). Experience : 1–3 years of hands-on experience in ML or data science, supported by a portfolio of relevant projects (model development, feature engineering, data analysis). Technical Skills : Proficiency in Python and ML frameworks / libraries (e.g., TensorFlow, PyTorch, scikit-learn); Experience with SQL for data manipulation; Familiarity with data visualization tools / libraries (e.g., Matplotlib, Seaborn, ggplot2). Analytical Skills : Strong ability to analyze large and complex datasets and derive meaningful insights. Communication : Ability to explain technical concepts and ML results to non-technical stakeholders. Teamwork : Demonstrated ability to work collaboratively, adapt to feedback, and contribute effectively in a team environment.

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