Job Description
<p><p><b>Description : </b> We are seeking a highly skilled and innovative Data Scientist with strong ML Engineering capabilities to design, develop, and deploy advanced machine learning models that drive actionable business insights.<br/><br/> This role bridges the gap between data science research and machine learning production deployment, requiring expertise in statistical modeling, data analysis, algorithm development, and MLOps.<br/><br/>The ideal candidate will work collaboratively with cross-functional teams to build scalable, high-performance machine learning systems that power intelligent business solutions.<br/><br/><b>Key Responsibilities : </b><br/><br/><b>Machine Learning & Model Development : </b><br/><br/>- Design, build, and train machine learning models for predictive analytics, classification, clustering, NLP, recommendation systems, or computer vision applications.<br/><br/>- Research and implement state-of-the-art algorithms and techniques in supervised, unsupervised, and reinforcement learning.<br/><br/>- Conduct feature engineering, model evaluation, and hyperparameter optimization to enhance performance.<br/><br/>- Implement best practices for model reproducibility, versioning, and validation.<br/><br/><b>Data Engineering & Pipeline Development : </b><br/><br/>- Build and maintain robust data pipelines for data ingestion, cleaning, transformation, and feature extraction using frameworks such as Apache Spark, Airflow, or Kedro.<br/><br/>- Collaborate with data engineers to ensure data quality, integrity, and availability across various sources and environments.<br/><br/>- Work with structured, semi-structured, and unstructured data, including large-scale datasets.<br/><br/><b>MLOps & Deployment : </b><br/><br/>- Package and deploy ML models into production environments using tools such as Docker, Kubernetes, TensorFlow Serving, or SageMaker.<br/><br/>- Monitor model performance post-deployment, detect model drift, and retrain models as necessary.<br/><br/>- Automate ML workflows (training, testing, deployment, monitoring) through CI/CD pipelines.<br/><br/>- Integrate machine learning outputs into real-time or batch systems using APIs or microservices.<br/><br/><b>Data Analysis & Business Insights : </b><br/><br/>- Perform exploratory data analysis (EDA) to identify patterns, correlations, and business opportunities.<br/><br/>- Collaborate with business stakeholders to understand challenges and translate them into machine learning problems.<br/><br/>- Communicate results and insights clearly using visualizations, dashboards, and presentations.<br/><br/><b>Collaboration & Continuous Improvement : </b><br/><br/>- Work closely with data engineers, product managers, and software developers to ensure end-to-end solution delivery.<br/><br/>- Stay abreast of advancements in AI/ML technologies, frameworks, and tools, integrating them into ongoing projects.<br/><br/>- Contribute to knowledge sharing, documentation, and mentoring of junior team members.<br/><br/><b>Required Qualifications & Skills : </b><br/><br/>- Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, Statistics, or a related field.<br/><br/>- 2+ years of hands-on experience in machine learning model development and deployment.<br/><br/>- Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, or XGBoost.<br/><br/>- Strong understanding of data structures, algorithms, and software engineering principles.<br/><br/>- Experience with data manipulation and analysis tools (Pandas, NumPy, SQL, Spark).<br/><br/>- Solid understanding of statistical methods, probability, and mathematical modeling.<br/><br/>- Proven experience in building and deploying models using MLOps frameworks.<br/><br/>- Familiarity with cloud ML services (AWS Sagemaker, Azure ML, GCP AI Platform).<br/><br/>- Hands-on experience in using Git, CI/CD tools, and containerization technologies.<br/><br/>- Excellent problem-solving skills, analytical thinking, and attention to detail.<br/><br/><b>Preferred Skills : </b><br/><br/>- Experience with deep learning architectures (CNNs, RNNs, Transformers).<br/><br/>- Familiarity with NLP frameworks (spaCy, Hugging Face Transformers, NLTK).<br/><br/>- Experience with data visualization tools such as Power BI, Tableau, or Plotly.<br/><br/>- Knowledge of feature store management and model monitoring tools.<br/><br/>- Experience in A/B testing and model interpretability (SHAP, LIME).<br/><br/>- Exposure to big data ecosystems (Hadoop, Databricks, Snowflake)</p><br/></p> (ref:hirist.tech)