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# Senior Data Scientist

- **Company:** Applexus Technologies, LLC
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Cluster Analysis, Software Code Optimization, Computer Programming, Continuous Integration, Data Architecture, Information Engineering, Extract Transform Load (ETL), Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, SQL Databases, Unstructured Data, Feature Store, Feature Engineering, Data Ingestion, Pytorch, Large Language Models, Deep Learning, Generative AI, Containerization, Scikit Learn, Information Technology, Xgboost, Machine Learning Operations, Drift Detection, Data Pipelines, Docker
- **Published:** October 8, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=dda7065409a16dd3

## About the Role

We are seeking an experienced and highly motivated Senior Data Scientist to design, develop, and scale advanced machine learning solutions that deliver measurable business outcomes. The ideal candidate will possess deep expertise across classical machine learning techniques, architect end-to-end ML pipelines, and build production-grade AI/ML solutions at scale., * Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field., * 8+ years of experience in Data Science, Machine Learning, or Artificial Intelligence.

 * Proven experience delivering AI/ML solutions in enterprise environments.
 * Demonstrated success in developing and deploying production-grade machine learning systems.

Technical Skills

Machine Learning & Statistics

 * Deep expertise in classical machine learning techniques including:
 + Regression
 + Classification
 + Clustering
 + Time-Series Forecasting
 + Ensemble Methods
 + Feature Engineering
 + Model Optimization
 * Strong understanding of statistics, probability, experimentation, and predictive modeling.

Programming & Frameworks

 * Proficiency in Python and SQL.
 * Experience with machine learning frameworks such as:
 + Scikit-learn
 + XGBoost
 + TensorFlow
 + PyTorch

MLOps & Deployment

 * Experience designing and implementing end-to-end ML pipelines.
 * Strong hands-on experience with:
 + Docker
 + CI/CD pipelines
 + Model serving and deployment frameworks
 + Drift monitoring and model lifecycle management
 * Experience with cloud platforms such as AWS, Azure, or GCP.

Data Engineering

 * Strong understanding of data architecture, ETL processes, APIs, and large-scale data processing.
 * Experience working with structured, semi-structured, and unstructured datasets., * Functional knowledge in at least one business domain:
 + Finance
 + Supply Chain
 * Experience with Generative AI and Large Language Models (LLMs) is a plus.
 * Experience working in consulting or customer-facing environments.

Key Competencies

 * Strong solution architecture and system design capabilities.
 * Ability to architect AI/ML solutions at scale.
 * Expertise in balancing accuracy, latency, scalability, and cost considerations.
 * Excellent communication and stakeholder management skills.
 * Strong analytical thinking and problem-solving abilities.
 * Ability to lead cross-functional teams and mentor junior team members.

## Description

* Work closely with business stakeholders to identify opportunities and translate business challenges into AI/ML solutions.
 * Design, develop, and deploy machine learning models using structured and unstructured data.
 * Apply advanced statistical and machine learning techniques including regression, classification, clustering, recommendation systems, and time-series forecasting.
 * Architect and implement end-to-end ML pipelines covering data ingestion, feature engineering, model training, validation, deployment, monitoring, and retraining.
 * Build scalable and production-ready ML systems using cloud-native technologies and MLOps best practices.
 * Drive model deployment through Docker, CI/CD pipelines, cloud-based serving infrastructure, and automated monitoring frameworks.
 * Establish robust monitoring mechanisms for model performance, data quality, drift detection, and retraining strategies.
 * Evaluate trade-offs between model accuracy, latency, scalability, and operational costs to deliver optimal business solutions.
 * Partner with data engineering teams to develop scalable data pipelines and feature stores.
 * Mentor junior data scientists and provide technical leadership on AI/ML initiatives.
 * Present findings, recommendations, and technical solutions to both business and executive stakeholders.