Responsibilities:

• Design and implement machine learning (ML) models and algorithms to extract insights from structured and unstructured data.
• Develop end-to-end ML pipelines, including data collection, preprocessing, feature engineering, model training, deployment, and optimization.
• Collaborate with data engineers and software developers to ensure data integrity, scalability, and successful integration of ML solutions into production environments.
• Perform data analysis, exploratory data analysis (EDA), and statistical evaluations to uncover trends, patterns, and relationships within data.
• Build, evaluate, and deploy predictive models, classification/regression algorithms, recommendation systems, and advanced analytics solutions.
• Continuously monitor model performance in production and fine-tune as needed for continuous improvement and business impact.
• Work with Natural Language Processing (NLP) techniques including text classification, named entity recognition (NER), sentiment analysis, topic modeling, and language generation.
• Leverage computer vision techniques (if applicable) including image classification, object detection, and optical character recognition (OCR).
• Familiarity with various AI models such as large language models (LLMs), generative AI, reinforcement learning, and deep learning architectures (e.g., CNNs, RNNs, Transformers).
• Utilize Microsoft Power Tools (Power BI, Power Query, Power Automate) for building dashboards, automating workflows, and creating visual analytics for stakeholders.
• Communicate findings and insights to both technical and non-technical stakeholders through reports, dashboards, visualizations, and presentations.
• Stay updated with the latest research and trends in AI, ML, deep learning, and data science.
• Collaborate with business teams to understand problems, define data-driven strategies, and deliver actionable insights.

Qualifications:

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Machine Learning, Artificial Intelligence, or a related field.
• Proven experience (5+ years) in data science, ML engineering, or similar roles involving production-grade ML solutions.
• Proficiency in programming languages such as Python, R, or Java.
• Hands-on experience with ML frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn.
• Strong understanding of statistical analysis, data preprocessing techniques, and feature engineering.
• Solid experience in using data analysis libraries and visualization tools such as Pandas, NumPy, Matplotlib, Seaborn, Plotly.
• Experience working with cloud platforms (AWS, GCP, or Azure) and big data tools like Spark, Hadoop, Databricks.
• Familiarity with SQL and NoSQL databases for data manipulation and integration.
• Exposure to NLP frameworks (SpaCy, NLTK, HuggingFace Transformers) and real-world NLP projects.
• Knowledge of model evaluation and tuning using metrics like accuracy, precision, recall, F1-score, AUC-ROC.
• Strong familiarity with Microsoft Power BI, Power Automate, and Power Query for dashboarding and workflow automation.
• Excellent problem-solving skills, attention to detail, and a structured approach to solving complex challenges.
• Strong communication and interpersonal skills, with the ability to translate complex technical concepts into business-friendly language.