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.
Qualifications
-
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.
-
Machine Learning Expertise:
-
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.
Additional Competencies
-
Self-Directed
-
Problem Solving
-
Interpersonal Skills
-
Strong Written and Verbal Communication Skills
-
Accuracy/Attention to Detail
-
Adaptability
-
Dependability
