What are the responsibilities and job description for the Data Scientist position at Conner Strong and Buckelew?

Department : Information Technology

Job Summary

We are seeking a highly skilled and motivated Data Scientist with a strong focus on machine learning (ML) and artificial intelligence (AI) to join our innovative team. The ideal candidate will excel in developing, deploying, and optimizing ML models and AI solutions, leveraging cutting-edge technologies like Azure Machine Learning, AutoML, and cloud-based AI tools. This role is essential for driving insights, automating processes, and delivering impactful business outcomes.

Key Responsibilities

ML and AI Model Development

Design, build, and deploy machine learning models using advanced algorithms, including AutoML techniques.
Develop and fine-tune models for regression, classification, clustering, and deep learning.
Explore and implement state-of-the-art AI approaches such as transformer models, generative AI, and reinforcement learning.
Expertise in natural language processing to analyze and generate insights from unstructured text data.

Advanced Analytics and Insights

Extract actionable insights from structured and unstructured datasets to support strategic decision-making.

Use predictive and prescriptive analytics to solve business challenges.

Technology and Tools

Utilize Azure Machine Learning and AI tools to manage model lifecycles.

Leverage cloud platforms like Azure, AWS, and GCP for scalable ML model deployment.

Employ frameworks like TensorFlow, PyTorch, and scikit-learn for model development.

Data Engineering and Preparation

Oversee data ingestion, cleaning, transformation, and feature engineering processes to ensure high-quality datasets.

Work with large datasets and implement scalable data pipelines.

Model Evaluation and Optimization

Evaluate model performance using metrics such as R2, RMSE, ROC-AUC, F1 score, and precision-recall.

Optimize models through hyperparameter tuning, feature selection, and iterative testing.

Collaboration and Deployment

Partner with cross-functional teams to integrate ML solutions into business applications.

Build and maintain APIs for deploying AI solutions at scale.

Documentation and Best Practices

Document all ML / AI processes and maintain a centralized repository.

Establish and follow best practices in model versioning, reproducibility, and model governance.

Qualifications

Education

Advanced degree (Master's or Ph.D.) in Computer Science, Data Science, Statistics, or a related field. Equivalent experience will be considered.

Experience

Minimum of 5 years in a data science or machine learning-focused role.

Technical Skills

Expertise in designing and deploying ML algorithms, AutoML tools, and AI applications.

Proficiency with programming languages such as Python and R, and ML libraries (TensorFlow, PyTorch, scikit-learn).

Hands-on experience with cloud platforms (Azure ML preferred) and big data ecosystems (e.g., Hadoop, Spark).

Strong understanding of CI / CD pipelines, DevOps practices, and infrastructure automation.

Familiarity with database systems (SQL Server, Snowflake) and API integrations.

Strong skills in ETL processes, data modeling, and DAX.

Experience with BI tools like Power BI or Tableau is a plus.

Knowledge of advanced ML techniques such as federated learning and generative adversarial networks (GANs).

Soft Skills

Analytical mindset with exceptional problem-solving abilities.

Excellent communication and collaboration skills to work in cross-functional teams.

Self-motivated with attention to detail and a commitment to continuous learning.

Ability to research and discuss complex technical topics when working with other teams.

Working Conditions

Office environment with moderate noise level; able to work flexible hours if needed.

Conner Strong & Buckelew is proud to be an equal opportunity employer. All qualified applicants will receive consideration without regard to race, color, religion, gender, affectional or sexual orientation, gender identity or expression, national origin, ancestry, nationality, age, disability (physical or mental), marital or domestic partnership or Civil Union status, pregnancy, family medical history or genetic information, atypical cellular or blood trait, military service or any other status protected by law.

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