Job
Description
Job Summary: We are seeking a highly skilled and passionate Data Scientist with 3-6 years of experience to join our dynamic team in Chennai. The ideal candidate will possess a strong background in machine learning and deep learning methodologies, coupled with expert-level proficiency in PySpark and SQL for large-scale data manipulation and analysis. You will be instrumental in transforming complex data into actionable insights, building predictive models, and deploying robust data-driven solutions that directly impact our business objectives. Key Responsibilities: Data Analysis & Feature Engineering: Perform extensive exploratory data analysis (EDA) to identify trends, patterns, and anomalies in large, complex datasets. Develop and implement robust data preprocessing, cleaning, and feature engineering pipelines using PySpark and SQL to prepare data for model training. Work with structured and unstructured data, ensuring data quality and integrity. Model Development & Implementation (Machine Learning & Deep Learning): Design, develop, and implement advanced Machine Learning (ML) and Deep Learning (DL) models to solve complex business problems, such as prediction, classification, recommendation, and anomaly detection. Apply a wide range of ML algorithms (e.g., Regression, Classification, Clustering, Ensemble methods) and DL architectures (e.g., CNNs, RNNs, Transformers) as appropriate for the problem at hand. Optimize and fine-tune models for performance, accuracy, and scalability. Experience with ML/DL frameworks such as TensorFlow, PyTorch, Scikit-learn, etc. Big Data Processing: Leverage PySpark extensively for distributed data processing, ETL operations, and running machine learning algorithms on big data platforms (e.g., Hadoop, Databricks, Spark clusters). Write efficient and optimized SQL queries for data extraction, transformation, and loading from relational and non-relational databases. Deployment & MLOps: Collaborate with MLOps engineers and software development teams to integrate and deploy machine learning and deep learning models into production environments. Monitor model performance, identify degradation, and implement retraining strategies to ensure sustained accuracy and relevance. Contribute to building CI/CD pipelines for ML model deployment. Insights & Communication: Translate complex analytical findings and model results into clear, concise, and actionable insights for both technical and non-technical stakeholders. Create compelling data visualizations and reports to effectively communicate findings and recommendations. Act as a subject matter expert, guiding business teams on data-driven decision-making. Research & Innovation: Stay abreast of the latest advancements in data science, machine learning, deep learning, and big data technologies. Proactively identify opportunities to apply new techniques and tools to enhance existing solutions or develop new capabilities. Required Qualifications: Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field. 3-6 years of hands-on experience as a Data Scientist or in a similar role. Expert proficiency in Python for data science, including libraries such as Pandas, NumPy, Scikit-learn. Strong expertise in PySpark for large-scale data processing and machine learning. Advanced SQL skills with the ability to write complex, optimized queries for data extraction and manipulation. Proven experience in applying Machine Learning algorithms to real-world problems. Solid understanding and hands-on experience with Deep Learning frameworks (e.g., TensorFlow, PyTorch) and architectures. Experience with big data technologies like Hadoop or Spark ecosystem. Strong understanding of statistical concepts, hypothesis testing, and experimental design. Excellent problem-solving, analytical, and critical thinking skills. Ability to work independently and collaboratively in a fast-paced environment. Strong communication and presentation skills, with the ability to explain complex technical concepts to diverse audiences.
