Senior Data Scientist

Dayworks; United States; Remote; USD 90,000 - 150,000; Full time

Job summary

An innovative firm is seeking a Senior Data Scientist to spearhead advanced AI/ML solutions that tackle critical business challenges. This exciting role involves leveraging complex datasets and cutting-edge statistical techniques to deliver impactful models. The ideal candidate will possess a robust mathematical background and a pragmatic approach to problem-solving, ensuring the development of scalable and interpretable AI solutions. Join a dynamic team dedicated to pushing the boundaries of technology and making a significant impact in the field of data science.

Qualifications

• 7+ years of experience in Data Science or Machine Learning roles.
• Strong academic foundation in statistics and mathematics.

Responsibilities

• Translate complex business problems into mathematical models.
• Design, build, and evaluate machine learning models for diverse use cases.

Skills

Data Science

Machine Learning

Applied Mathematics

Python

Statistical Analysis

Model Evaluation Metrics

Cloud-based ML Services

Education

Master's in Data Science or Related Field

PhD in Mathematics or Statistics

Tools

pandas

NumPy

scikit-learn

TensorFlow

PyTorch

HuggingFace

AWS SageMaker

Google Vertex AI

Azure ML

Job description

This role is for one of the Weekday's clients

Min Experience: 7 years

Location: Remote (India)

JobType: full-time

We are looking for an experienced and highly skilled Senior Data Scientist to develop and deploy advanced AI/ML solutions that address critical business challenges. This role involves working with complex datasets, applying cutting-edge statistical and machine learning techniques, and delivering models that create measurable business value. The ideal candidate will combine strong mathematical foundations with a pragmatic approach to solving real-world problems.

Requirements

Key Responsibilities

• Translate complex business problems into mathematical models, predictive algorithms, or optimization frameworks.
• Perform in-depth exploratory data analysis (EDA), feature selection, and feature engineering using advanced statistical techniques.
• Design, build, tune, and evaluate machine learning and deep learning models for diverse use cases, including NLP, computer vision, forecasting, and recommendation systems.
• Research and experiment with state-of-the-art approaches (e.g., Transformers, LLMs, Graph Neural Networks), adapting them for production use.
• Apply model interpretability tools (e.g., SHAP, LIME, Explainable AI) and present insights to stakeholders in a clear and actionable manner.
• Conduct statistical testing (A/B testing, hypothesis testing) to assess the real-world impact of deployed models.
• Collaborate with engineering teams to deploy models via APIs or serving platforms, and contribute to MLOps practices.
• Keep up with advancements in machine learning and AI to incorporate innovative methods into project work.

Required Skills

• 7+ years of experience in Data Science, Machine Learning, or Applied Mathematics roles.
• Strong academic foundation in statistics, probability, linear algebra, optimization, and calculus.
• Proficiency in Python and ML libraries (pandas, NumPy, scikit-learn, TensorFlow, PyTorch, HuggingFace).
• Proven experience in building and deploying production-grade models with demonstrated business impact.
• Deep understanding of ML evaluation metrics (ROC-AUC, F1, Precision-Recall, cost-sensitive evaluation).
• Familiarity with cloud-based ML services (e.g., AWS SageMaker, Google Vertex AI, Azure ML).
• Knowledge of model fairness, bias detection, and responsible AI best practices.

Preferred Skills

• Hands-on experience with LLMs, NLP, computer vision, or time-series forecasting at scale.
• Research or publication experience in peer-reviewed conferences or journals.
• Familiarity with advanced topics like AutoML, reinforcement learning, or Bayesian optimization.
• Strong analytical mindset with the ability to work with complex, unstructured, and noisy real-world data.
• Excellent balance of scientific rigor and business acumen to guide model development and deployment.
• Commitment to building scalable, interpretable, and impactful AI solutions.