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# Senior Data Scientist

- **Company:** ALTAK GROUP INC.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $124,800.0 - $156,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Computer Vision, Microsoft Azure, Big Data, Python (Programming Language), PostgreSQL, Machine Learning, MySQL, Natural Language Processing, NumPy, Recommender Systems, Power BI, OpenAI, Tensorflow, SQL Databases, Tableau (Software), Google Cloud, Chatbots, Pytorch, LangChain, Large Language Models, Snowflake, Llamaindex, Generative AI, Agentic-AI, Pandas, Scikit Learn, Kubernetes, Information Technology, CrewAI, Data Analytics, Xgboost, AutoGen, Machine Learning Operations, Claude, Google Gemini, Looker Analytics, Data Pipelines, Docker
- **Published:** October 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=9a1b0e4f8d04d392

## About the Role

We are seeking an experienced and highly analytical Senior Data Scientist to lead the development of advanced data-driven solutions and AI-powered products. The ideal candidate will have a strong background in machine learning, artificial intelligence,

predictive analytics, and statistical modeling, along with proven experience leading data science teams and delivering business-impacting AI initiatives.

This role requires a combination of technical expertise, strategic thinking, and leadership skills to transform complex data into actionable insights and scalable AI solutions., * Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.

 * 5+ years of hands-on experience in Data Science, Machine Learning, Artificial Intelligence, or related disciplines.
 * Proven experience leading and mentoring data science teams.
 * Strong expertise in Python and data science libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, and XGBoost.
 * Experience working with SQL and large-scale datasets.
 * Strong understanding of machine learningalgorithms, statistical analysis,predictive modeling, and experimentation methodologies.
 * Experience deploying machinelearning models into production environments.
 * Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
 * Excellent communication, stakeholder management, and problem-solving skills., * Experience with Generative AI, Large LanguageModels (LLMs), Retrieval-Augmented Generation (RAG), and AI Agents.
 * Hands-on experience with AI tools such as OpenAI, Anthropic Claude,Gemini, LangChain, LlamaIndex, CrewAI, AutoGen, or similar frameworks.
 * Experience building NLP, Computer Vision,Recommendation Systems, or Conversational AI solutions.
 * Familiarity with MLOps tools such as MLflow,Kubeflow, Airflow, Docker,and Kubernetes.
 * Experience in startupor high-growth environments.
 * Advanced degree (Master's or PhD) is a plus.

Preferred AI & Data ScienceTech Stack

 * Python, R
 * SQL, PostgreSQL, MySQL,Snowflake
 * TensorFlow, PyTorch, Scikit-learn
 * OpenAI, Claude, Gemini APIs
 * LangChain, LlamaIndex, CrewAI
 * AWS SageMaker, Vertex AI, AzureAI
 * Docker, Kubernetes, MLflow
 * Power BI, Tableau, Looker

## Description

* Lead the design, development, and deployment of machine learning and AI models for business-critical applications.
 * Manage and mentor a team of Data Scientists, Machine Learning Engineers, and Data Analysts.
 * Collaborate with Product, Engineering, Operations, and Executive stakeholders to identify opportunities for AI-driven innovation.
 * Build predictive models, recommendation systems, forecasting solutions, NLP applications, and other advanced analytics products.
 * Develop and maintain scalable data science workflows and MLOps pipelines.
 * Evaluate and implement modern AI tools, frameworks, and Large Language Model (LLM) technologies.
 * Conduct exploratory data analysis (EDA) to uncover trends, patterns, and actionable insights.
 * Define KPIs, success metrics, and model performance benchmarks.
 * Communicate technical findings and recommendations to both technical and non-technical stakeholders.
 * Ensure data quality, governance, compliance, and security best practices are followed.
 * Stay current with emerging AI technologies, research advancements, and industry best practices.