Data Scientist- PhD
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.
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