Senior Data Scientist

Applexus Technologies (P) Ltd; Federal Way (WA); On-site; USD 120,000 - 150,000; Full time

Job summary

Applexus Technologies (P) Ltd is seeking a Senior Data Scientist in Federal Way, WA, to design and implement machine learning solutions that solve key business challenges. The ideal candidate should have at least 8 years of experience in Data Science and be adept at deploying scalable AI systems.

In this role, you will collaborate with various teams to leverage data for decision-making, build end-to-end ML pipelines, and mentor junior staff. Proficiency in Python, SQL, and cloud platforms is required.

Qualifications

• 8+ years of experience in Data Science, Machine Learning, or AI.
• Demonstrated success in deploying production-grade machine learning systems.
• Experience with cloud platforms like AWS, Azure, or GCP.

Responsibilities

• Design, develop, and deploy machine learning models using data.
• Create end-to-end ML pipelines covering all stages.
• Drive model deployment through CI/CD pipelines and cloud-based serving.

Skills

Machine Learning Techniques

Statistical Analysis

Python

SQL

Cloud Platforms (AWS, Azure, GCP)

MLOps

Education

Bachelor's or Master's degree in Computer Science or related field

Tools

Scikit-learn

XGBoost

TensorFlow

PyTorch

Docker

Job description

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.

This role requires close collaboration with business stakeholders, analytics teams, data engineers, and technology leaders to solve complex business problems across domains such as Finance and Supply Chain.

Key Responsibilities

• 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.

Qualifications

Education

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.

Experience

8+ years of experience in Data Science, Machine Learning, or Artificial Intelligence.

Demonstrated success in developing and deploying production-grade machine learning systems.

Technical Skills

Deep expertise in classical machine learning techniques including:

• Regression
• Classification
• Time-Series Forecasting
• 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
• Model serving and deployment frameworks
• Drift monitoring and model lifecycle management

Experience with cloud platforms such as AWS, Azure, or GCP.

Strong understanding of data architecture, ETL processes, APIs, and large-scale data processing.

Experience working with structured, semi-structured, and unstructured datasets.

Preferred Qualifications

• 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.
• 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.