Job Description

We are seeking a motivated Data Scientist with 1-3 years of experience to develop, optimize, and deploy machine learning models and data products. You will leverage ML Ops best practices to build scalable, production-ready solutions that address complex business challenges. This role involves close collaboration with cross-functional teams to deliver actionable insights and data-driven solutions. Join a dynamic team that values innovation, collaboration, and continuous learning as we tackle business problems using advanced data science and machine learning techniques. Key Responsibilities

• Develop and optimize machine learning models using a variety of techniques, including large language models, neural networks, tree-based algorithms, and statistical methods.
• Analyze complex datasets (structured, semi-structured, and unstructured) by applying feature engineering and statistical techniques to extract actionable insights for model development.
• Design and maintain end-to-end machine learning pipelines emphasizing modularity, reproducibility, and efficient retraining.
• Prototyping and developing domain-specific AI agents that can perform tasks such as information gathering, data extraction, and intelligent actions.
• Deploy models into production environments using ML Ops practices such as version control, logging, monitoring, and lifecycle management to ensure scalability, reliability, and performance.
• Perform data exploration, preprocessing, and visualization to uncover trends and clearly communicate findings to both technical and non-technical stakeholders.
• Collaborate with data engineers, software developers, and product owners to integrate machine learning solutions into business applications and cloud platforms.
• Research and experiment with emerging algorithms, frameworks, and tools to enhance model accuracy, efficiency, and scalability.
• Maintain and improve existing machine learning models and analytics solutions to adapt to evolving business needs.
• Contribute to team growth by participating in code reviews, maintaining documentation, and fostering a collaborative, data-driven culture.

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• 1-3 years of applied data science and machine learning.
• Strong proficiency in Python, including experience with libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, and HuggingFace. Experience with languages such as R, JavaScript, Java, etc. is a plus.

• Hands-on experience with supervised and unsupervised learning techniques, including regression, classification, clustering, decision trees, and neural networks.
• Proficiency in model optimization, feature engineering, hyperparameter tuning, and evaluation metrics to ensure robust and accurate results.
• Understanding of LLM architectures, fine-tuning, prompt engineering, and context retrieval.

• Expertise in cleaning, transforming, and analyzing large datasets (structured and unstructured) to enable meaningful insights.
• Practical experience with ML Ops practices to streamline and scale machine learning workflows, including model deployment and monitoring.
• Familiarity with cloud platforms and tools for data processing, model training, deployment, and monitoring (e.g., Azure ML, MLflow).
• Strong ability to collaborate with cross-functional teams and clearly present complex technical concepts to technical and non-technical audiences.

• Self-Directed
• Problem Solving
• Interpersonal Skills
• Strong Written and Verbal Communication Skills
• Accuracy/Attention to Detail
• Adaptability
• Dependability

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