What are the responsibilities and job description for the Principal Data Scientist position at Careers Integrated Resources Inc?
Title: Principal Data Scientist - AI/ML
Location: Houston TX - Hybrid (4 Days a week Onsite)
Contract: 6 Months and Possibility of Extension
JOB DESCRIPTION
Non-negotiable Must Have Requirements:
Strong background in time-series modeling is essential.
Excellent communication skills are highly valued.
A background in Generative AI and computer vision is helpful, but it is not required.
Summary:
We are looking for a proactive Data Scientist with strong AI/ML and Large Language Model (LLM) expertise, particularly in time-series modeling.
The ideal candidate should have experience analyzing diverse datasets, building machine learning models, and deploying AI solutions for business value.
Responsibilities include developing time-series models from business data, with opportunities to work on deep learning, computer vision, natural language processing, LLMs, and multi-agent systems.
You will design, train, and deploy scalable time-series models and turn data insights into actionable strategies.
Key Responsibilities:
Data Analysis & Feature Engineering: Collect, process, and analyze structured and unstructured data, engineering relevant features to improve model performance.
Develop, train, and optimize machine learning and deep learning models for time-series analysis and anomaly detection.
LLM and Agent-based Application: Build AI solutions using LLMs, emphasizing prompt engineering, multi-agent systems, fine-tuning, and inference optimization.
Business Impact & Decision Support: Translate complex data science methodologies into actionable insights, collaborating with stakeholders to drive business value.
Data Storytelling & Visualization: Develop clear, compelling presentations and dashboards to communicate findings to non-technical stakeholders.
Required Technical Skills:
A solid foundation in time-series modeling and anomaly detection is required.
Proficiency in Python and experience with time-series modeling techniques such as linear regression, random forest, and support vector machines are required.
Strong communication abilities and a track record of collaborating with stakeholders and business owners are important.
Deep learning, Generative AI, computer vision, data engineering, and ML Ops experience is helpful but not required.
Required Soft Skills:
Curious & Innovative: Passionate about solving complex business problems using data and AI.
Ownership & Initiative: Proactively drive projects from conception to deployment.
Business Acumen: Understand how AI/ML solutions impact business goals and decision-making.
Effective Communication: Ability to explain technical models and AI methodologies to non-technical audiences.
Preferred Qualifications:
Graduate degree (Masters or Ph.D.) in a quantitative field (e.g., Computer Science, Data Science, Statistics, Engineering, Mathematics, Economics).
Experience with time-series modeling and abnormal detection.
Familiarity with deep learning and generative AI.
This role is ideal for a Data Scientist who wants to work at the cutting edge of AI and ML, leveraging LLMs, NLP, and predictive analytics to drive meaningful impact.
Location: Houston TX - Hybrid (4 Days a week Onsite)
Contract: 6 Months and Possibility of Extension
JOB DESCRIPTION
Non-negotiable Must Have Requirements:
Strong background in time-series modeling is essential.
Excellent communication skills are highly valued.
A background in Generative AI and computer vision is helpful, but it is not required.
Summary:
We are looking for a proactive Data Scientist with strong AI/ML and Large Language Model (LLM) expertise, particularly in time-series modeling.
The ideal candidate should have experience analyzing diverse datasets, building machine learning models, and deploying AI solutions for business value.
Responsibilities include developing time-series models from business data, with opportunities to work on deep learning, computer vision, natural language processing, LLMs, and multi-agent systems.
You will design, train, and deploy scalable time-series models and turn data insights into actionable strategies.
Key Responsibilities:
Data Analysis & Feature Engineering: Collect, process, and analyze structured and unstructured data, engineering relevant features to improve model performance.
Develop, train, and optimize machine learning and deep learning models for time-series analysis and anomaly detection.
LLM and Agent-based Application: Build AI solutions using LLMs, emphasizing prompt engineering, multi-agent systems, fine-tuning, and inference optimization.
Business Impact & Decision Support: Translate complex data science methodologies into actionable insights, collaborating with stakeholders to drive business value.
Data Storytelling & Visualization: Develop clear, compelling presentations and dashboards to communicate findings to non-technical stakeholders.
Required Technical Skills:
A solid foundation in time-series modeling and anomaly detection is required.
Proficiency in Python and experience with time-series modeling techniques such as linear regression, random forest, and support vector machines are required.
Strong communication abilities and a track record of collaborating with stakeholders and business owners are important.
Deep learning, Generative AI, computer vision, data engineering, and ML Ops experience is helpful but not required.
Required Soft Skills:
Curious & Innovative: Passionate about solving complex business problems using data and AI.
Ownership & Initiative: Proactively drive projects from conception to deployment.
Business Acumen: Understand how AI/ML solutions impact business goals and decision-making.
Effective Communication: Ability to explain technical models and AI methodologies to non-technical audiences.
Preferred Qualifications:
Graduate degree (Masters or Ph.D.) in a quantitative field (e.g., Computer Science, Data Science, Statistics, Engineering, Mathematics, Economics).
Experience with time-series modeling and abnormal detection.
Familiarity with deep learning and generative AI.
This role is ideal for a Data Scientist who wants to work at the cutting edge of AI and ML, leveraging LLMs, NLP, and predictive analytics to drive meaningful impact.