What are the responsibilities and job description for the Data Scientist- PhD position at GA Telesis, LLC?
GA Telesis is a global leader providing integrated solutions to the aviation and aerospace industries. We serve over 3,000 customers, including airlines, original equipment manufacturers, maintenance, repair, and overhaul (MRO) providers, and suppliers worldwide, with 31 leasing, sales, distribution, and MRO operations in 19 countries. The GA Telesis Ecosystem™ concept is core to our providing integrated aviation solutions to our global customers.
We are seeking a motivated and analytical Data Scientist to join our dynamic team. In this role, you will work closely with our data scientists, data analysts and cross-functional teams to analyze data, build models, and generate insights that drive business decisions. This is an excellent opportunity for someone looking to expand their data science career and to develop their skills in a supportive and innovative environment.
**Important Notice:
Eligibility Requirement: Applicants must be legally authorized to work in the U.S. The company does not provide visa sponsorship or accept candidates requiring sponsorship.
Key Responsibilities
We are seeking a motivated and analytical Data Scientist to join our dynamic team. In this role, you will work closely with our data scientists, data analysts and cross-functional teams to analyze data, build models, and generate insights that drive business decisions. This is an excellent opportunity for someone looking to expand their data science career and to develop their skills in a supportive and innovative environment.
**Important Notice:
Eligibility Requirement: Applicants must be legally authorized to work in the U.S. The company does not provide visa sponsorship or accept candidates requiring sponsorship.
Key Responsibilities
- Data Analysis and Exploration:
- Collect, clean, and preprocess data from various sources.
- Perform exploratory data analysis (EDA) to uncover trends, patterns, and insights.
- Visualize data using tools such as Matplotlib to communicate findings to stakeholders.
- Model Development:
- Assist in building, testing, and validating machine learning models under the guidance of our data scientist.
- Implement statistical and machine learning algorithms using Python, R, or similar programming languages.
- Participate in the optimization and fine-tuning of models to improve performance.
- Data Reporting and Visualization:
- Create reports and dashboards to present data insights to non-technical stakeholders.
- Collaborate with data engineering and IT teams to integrate data solutions into production environments.
- Support the automation of data pipelines and workflows.
- Collaboration and Communication:
- Work closely with cross-functional teams, including product management, marketing, and engineering, to understand business needs and deliver data-driven solutions.
- Participate in team meetings, contribute to project planning, and provide regular updates on progress.
- Continuous Learning and Development:
- Stay updated with the latest trends and advancements in data science, machine learning, and AI from both industry and academic publications.
- Take part in training sessions, workshops, and conferences to enhance skills and knowledge.
- Education:
- Required: PhD in technical subjects: Physics, Mathematics, Engineering, Statistics, Computer Science, etc.
- 3 to 5 years’ experience in data science
- Relevant certifications in data science/machine learning – a plus.
- Technical Skills:
- Proficiency in programming languages such as Python, R, or SQL.
- Basic understanding of Gen AI and Agentic AI
- Familiarity with data analysis libraries (e.g., Pandas, NumPy) and machine learning frameworks (e.g., Scikit-learn, TensorFlow).
- Basic understanding of statistical analysis, hypothesis testing, and data modeling.
- Experience with data visualization tools like Matplotlib or Tableau.
- Experience working with cloud platforms (e.g., AWS, GCP, Azure).
- Ability to extract applicable AI insights ( models, techniques and frameworks) from academic research.
- Analytical Skills:
- Strong problem-solving skills with the ability to analyze complex data sets and derive actionable insights.
- Attention to detail and a commitment to producing high-quality work.
- Communication Skills:
- Ability to communicate technical information to non-technical audiences.
- Ability to present findings in a clear concise way to management.