SMC³
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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.
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
Seniority level Entry level
Employment type Full-time
Job function Engineering and Information Technology
Industries Transportation, Logistics, Supply Chain and Storage
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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.
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
Seniority level Entry level
Employment type Full-time
Job function Engineering and Information Technology
Industries Transportation, Logistics, Supply Chain and Storage
#J-18808-Ljbffr