Data Scientist

Howmet Aerospace • Pittsburgh, PA

1d • Onsite

About The Position

Howmet Aerospace Inc. (“Howmet”) is currently in search of a Data Scientist join the Global Information Systems (GIS) organization at the Howmet Corporate Center in Pittsburgh, PA . In this role you will be responsible for developing and deploying artificial intelligence models and applications.

Requirements

• Strong analytical and problem-solving skills with attention to detail.
• Excellent verbal and written communication skills with the ability to explain technical concepts to a variety of audiences.
• Demonstrates curiosity, initiative, and a willingness to continuously learn new technologies and analytical techniques.
• Ability to manage multiple assignments while meeting deadlines.
• Strong organizational skills and ability to document work effectively.
• Ability to work collaboratively in a team-oriented environment.
• Basic understanding of statistical analysis and machine learning concepts.
• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Information Systems, or a related quantitative discipline.
• 0–2 years of experience in data science, analytics, software development, or a related field, including internships, co-op programs, undergraduate research, or equivalent project experience.
• Working knowledge of Python and SQL.
• Basic understanding of statistics, probability, machine learning concepts, and data visualization.
• Employees must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is not available for this position

Nice To Haves

• Internship, co-op, academic research, or personal project experience involving analytics, machine learning, or artificial intelligence.
• Experience with Python libraries such as: Machine Learning: Scikit-learn, TensorFlow, PyTorch, or similar frameworks. Data Analysis: Pandas, NumPy. Visualization: Matplotlib, Seaborn, or Plotly.
• Basic familiarity with cloud platforms such as Oracle Cloud Infrastructure (preferred), AWS, Azure, or Google Cloud.
• Exposure to Jupyter Notebooks, Git, Docker, or basic MLOps concepts.
• Exposure to Large Language Models (LLMs) and Generative AI concepts through coursework, internships, research, or personal projects.
• Familiarity with AI frameworks and libraries such as LangChain, LlamaIndex, Hugging Face Transformers, or similar tools.
• Experience using commercial or open-source LLMs (e.g., OpenAI, Anthropic, Meta Llama, Mistral) through APIs or local deployments.
• Basic understanding of Retrieval-Augmented Generation (RAG), prompt engineering, vector databases, embeddings, and semantic search concepts.
• Experience with Power BI or similar visualization tools is beneficial.

Responsibilities

• Assist in developing, training, and evaluating machine learning models to solve business problems under the guidance of senior team members.
• Perform exploratory data analysis to identify trends, patterns, and opportunities within business data.
• Support feature engineering, model validation, and performance evaluation.
• Collect, clean, transform, and validate data from multiple sources to ensure quality and usability.
• Develop and maintain datasets for analytics and machine learning projects.
• Collaborate with data engineers and business teams to understand data requirements and improve data quality.
• Develop dashboards, reports, and visualizations that communicate insights to business stakeholders.
• Present findings and recommendations using clear data storytelling techniques.
• Assist with ad hoc analyses to support operational and strategic initiatives.
• Support the deployment, testing, and monitoring of machine learning models in production environments.
• Assist in documenting models, code, and analytical processes.
• Help troubleshoot data and model performance issues.
• Stay current with emerging trends in data science, machine learning, analytics, and artificial intelligence.
• Learn and apply new analytical techniques, tools, and best practices.
• Participate in knowledge sharing and team learning activities.
• Work closely with data scientists/AIML Engineers, data engineers, software developers, and business stakeholders on cross-functional projects.
• Communicate technical findings in a clear and concise manner appropriate for both technical and non-technical audiences.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level