Senior Data Scientist

MAES Solutions; New Jersey; On-site; USD 120,000 - 180,000; Full time

Job summary

MAES Solutions is seeking a highly skilled Data Scientist to develop and implement advanced machine learning, statistical modeling, predictive analytics, and AI solutions that drive data-driven business decisions. You will build ML models, perform EDA and feature engineering, and deploy end-to-end data science workflows using Python, TensorFlow, PyTorch, NLP, and Generative AI techniques.

This on-site role in New Jersey requires strong analytical and communication skills and a track record of

Qualifications

• 8+ years of experience in Data Science, Machine Learning, Predictive Analytics, Statistical Modeling, and Advanced Analytics.
• Strong programming expertise in Python with hands-on experience using Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, and Seaborn.
• Extensive knowledge of ML algorithms including Regression, Trees, Random Forest, Gradient Boosting, XGBoost, LightGBM, SVM, K-Means, PCA, and Ensemble Methods.
• Hands-on experience with Deep Learning frameworks such as TensorFlow, PyTorch, and Keras, including neural networks and optimization.
• Experience with NLP including text classification, sentiment analysis, tokenization, embeddings, transformers, and language models.
• Strong understanding of Generative AI, LLMs, Prompt Engineering, RAG, Vector Databases, Embeddings, and AI app development.

Responsibilities

• Develop, train, validate, and deploy ML models for classification, regression, clustering, forecasting, recommendations, anomaly detection, and predictive analytics.
• Perform exploratory data analysis (EDA), statistical analysis, data profiling, preprocessing, cleansing, and feature engineering.
• Build predictive models using Python libraries and ML frameworks.
• Apply advanced statistical modeling, hypothesis testing, regression, time-series, correlation analysis, and experimental design.
• Develop and optimize Deep Learning models using TensorFlow, PyTorch, Keras, and related AI/ML tech.
• Design and implement NLP solutions for text classification, sentiment analysis, document processing, information extraction, summarization, and semantic analysis.
• Work with Generative AI, LLMs, embeddings, vector databases, prompt engineering, and Retrieval-Augmented Generation (RAG).
• Develop end-to-end data science solutions from data collection to deployment, monitoring and improvement.
• Evaluate model performance using metrics, cross-validation, hyperparameter tuning, and explainability techniques.
• Implement MLOps practices including versioning, experiment tracking, deployment automation, monitoring, drift detection, and lifecycle management.
• Work with cloud-based AI/ML platforms such as Azure ML, Azure Databricks, and Azure Synapse Analytics.
• Develop scalable data processing and ML workflows using Apache Spark, PySpark, Databricks, and distributed computing.
• Create dashboards and visualizations using Power BI, Tableau, Matplotlib, Seaborn to communicate insights.

Skills

Python

Pandas

NumPy

Scikit-learn

Deep Learning

TensorFlow

PyTorch

XGBoost

NLP

MLOps

Tools

Azure Machine Learning

Apache Spark

Databricks

Power BI

Job description

About the Role: We are seeking a highly skilled and experienced Data Scientist to develop and implement advanced machine learning, statistical modeling, predictive analytics, and AI solutions that drive data-driven business decisions.

Key Responsibilities:

• Develop, train, validate, and deploy machine learning models for classification, regression, clustering, forecasting, recommendation, anomaly detection, and predictive analytics use cases.
• Perform exploratory data analysis (EDA), statistical analysis, data profiling, data preprocessing, data cleansing, and feature engineering to identify meaningful patterns and business insights.
• Build predictive models using Python, Scikit-learn, Pandas, NumPy, XGBoost, LightGBM, and other machine learning frameworks.
• Apply advanced statistical modeling, hypothesis testing, regression analysis, probability, time-series analysis, correlation analysis, and experimental design to solve complex business problems.
• Develop and optimize Deep Learning models using frameworks such as TensorFlow, PyTorch, Keras, and related AI/ML technologies.
• Design and implement Natural Language Processing (NLP) solutions for text classification, sentiment analysis, document processing, information extraction, text summarization, and semantic analysis.
• Work with Generative AI, Large Language Models (LLMs), embeddings, vector databases, prompt engineering, Retrieval-Augmented Generation (RAG), and AI-powered applications where applicable.
• Develop end-to-end data science solutions, from data collection and preprocessing through model development, evaluation, deployment, monitoring, and continuous improvement.
• Evaluate model performance using appropriate evaluation metrics, cross-validation, hyperparameter tuning, model selection, explainability, and statistical validation techniques.
• Implement MLOps practices model versioning, experiment tracking, deployment automation, model monitoring, drift detection, and lifecycle management.
• Work with cloud-based AI/ML platforms such as Microsoft Azure, Azure Machine Learning, Azure Databricks, Azure Data Lake, and Azure Synapse Analytics.
• Develop scalable data processing and machine learning workflows using Apache Spark, PySpark, Databricks, and distributed computing technologies.
• Create dashboards, visualizations, and analytical reports using Power BI, Tableau, Matplotlib, Seaborn, or other visualization tools to communicate insights to business stakeholders.

Required Skills & Qualifications:

• 8+ years of experience in Data Science, Machine Learning, Predictive Analytics, Statistical Modeling, and Advanced Analytics.
• Strong programming expertise in Python, with hands-on experience using Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, and Seaborn.
• Extensive knowledge of Machine Learning algorithms, including Linear/Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, XGBoost, LightGBM, SVM, K-Means, Clustering, PCA, and Ensemble Methods.
• Hands-on experience with Deep Learning frameworks such as TensorFlow, PyTorch, and Keras, including neural networks and model optimization.
• Experience with Natural Language Processing (NLP), including text classification, sentiment analysis, tokenization, embeddings, transformers, and language models.
• Strong understanding of Generative AI, Large Language Models (LLMs), Prompt Engineering, RAG, Vector Databases, Embeddings, and AI application development.