ML Intern, Research
Montreal, QC · On-site
... analysis, visualization, and model deployment. * Demonstrated capability to understand and ... Strong knowledge of linear algebra, calculus, and statistics. * Passion for applying ML research to ...
ML Intern, Research
Montreal, QC · On-site
... analysis, visualization, and model deployment. * Demonstrated capability to understand and ... Strong knowledge of linear algebra, calculus, and statistics. * Passion for applying ML research to ...
STEM Experts - PhD - AI Training - Canada
Montreal, QC · On-site +1
CA$150/hr
For this project we are seeking experts in subjects like Computer Science, Statistics, Mechanical Engineering, Maths, Physics and more who are comfortable narrating their approach to solving complex ...
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STEM Experts - PhD - AI Training - Canada
Montreal, QC · On-site +1
CA$150/hr
For this project we are seeking experts in subjects like Computer Science, Statistics, Mechanical Engineering, Maths, Physics and more who are comfortable narrating their approach to solving complex ...
... analytics and statistics when necessary. Ability to use software such as: - Docker - Unit Testing - Code versioning tools (Git/GitLab) Your job will entail developing back end systems. You will also ...
... analytics and statistics when necessary. Ability to use software such as: - Docker - Unit Testing - Code versioning tools (Git/GitLab) Your job will entail developing back end systems. You will also ...
Statistical Analyst Intern information
What are the key skills and qualifications needed to thrive as a Statistical Analyst Intern, and why are they important?
To thrive as a Statistical Analyst Intern, you need a solid understanding of statistics, data analysis, and a background in mathematics or a related field, often demonstrated through relevant coursework or a degree in progress. Familiarity with statistical software such as R, SAS, SPSS, or Python, and proficiency in Excel are typically required. Attention to detail, problem-solving abilities, and strong communication skills help you interpret data accurately and share insights effectively. These skills are vital for producing reliable analyses that inform business decisions and support organizational goals.
What types of projects and data sets do Statistical Analyst Interns typically work with during their internship?
Statistical Analyst Interns often engage with diverse projects ranging from market research analysis to operational data studies, depending on the industry. They commonly work with large data sets such as sales figures, customer behavior logs, or experimental results, applying statistical methods to uncover trends and insights. Interns usually collaborate closely with full-time analysts, data scientists, and cross-functional teams to support ongoing initiatives and may have opportunities to present findings to stakeholders. This hands-on experience helps interns develop technical skills and gain exposure to real-world applications of statistical analysis.
What does a Statistical Analyst Intern do?
A Statistical Analyst Intern assists with collecting, analyzing, and interpreting data to help organizations make informed decisions. Their tasks typically include preparing datasets, running statistical tests, and creating reports or visualizations to communicate findings. Interns often use statistical software like R, SAS, or Python, and work under the supervision of experienced analysts or data scientists. This role helps interns gain practical experience in data analysis, problem-solving, and applying statistical methods to real-world problems.
What is the difference between Statistical Analyst Intern vs Data Analyst Intern?
| Aspect | Statistical Analyst Intern | Data Analyst Intern |
|---|---|---|
| Required Credentials | Typically pursuing or holding a degree in statistics, mathematics, or related fields | Usually pursuing or holding a degree in data science, analytics, or related fields |
| Work Environment | Internships in finance, healthcare, or research organizations focusing on statistical analysis | Internships in tech, marketing, or business sectors analyzing data sets |
| Employer & Industry Usage | Used by companies needing statistical modeling and hypothesis testing | Used by companies focusing on data visualization, reporting, and insights |
The main difference between a Statistical Analyst Intern and a Data Analyst Intern lies in their focus areas. Statistical Analyst Interns primarily work on statistical modeling and hypothesis testing, often in research-heavy industries. Data Analyst Interns tend to focus on data collection, cleaning, and visualization to support business decisions. Both roles require similar educational backgrounds but differ in their specific tasks and industry applications.
Other
Posted 25 days ago
Job description
About Valence Labs
Valence Labs is Recursion's frontier AI research engine. We work on high-impact research programs that will materially alter Recursion's ability to discover and develop medicines for complex diseases. We balance near-term pragmatism with a long-term view on where we believe the field will be in 3-5 years and incubate, design, and productize those approaches that we believe will be most impactful. Our research is driven by optimism, purpose, and a shared vision for a healthier tomorrow, and our work is regularly published in top journals and conferences. Our team is located in London and Montreal, where we share close connections with Mila, the world's largest deep learning research institute.
About the role
We're seeking motivated interns to contribute to the development of AI systems across areas, including multi-omic foundation models, next-generation structural biology methods, and approaches for autonomous science. We're looking for individuals with strong engineering skills, including expertise in designing, implementing, improving, and deploying distributed machine learning systems at scale. In addition, we highly value proficiency with state-of-the-art machine learning algorithms and exceptional problem-solving skills.
In this role, you will:
- Support Valence Labs' primary research programs in ML for drug discovery.
- Create and improve novel ML methods that will accelerate drug discovery.
- Collaborate with an interdisciplinary team of dry and wet lab scientists to inform and improve our models and systems.
- Present and communicate research findings through talks, blog posts, publications, and conferences.
A successful candidate will have most of the following:
- Currently enrolled in a post-doctoral fellowship, PhD, or Master's degree program.
- Strong programming skills and understanding of modern software development practices, especially in Python.
- Experience in building and deploying high-performance implementations of deep learning algorithms.
- Proven track record in machine learning, including designing new architectures, hands-on experimentation, analysis, visualization, and model deployment.
- Demonstrated capability to understand and summarize scientific content and implement deep learning models based on descriptions from publications.
- Strong knowledge of linear algebra, calculus, and statistics.
- Passion for applying ML research to real-world problems.
Nice to have:
- Authorship of a publication in peer-reviewed conferences (e.g., NeurIPS, ICML, ICLR, or similar).
- Contribution to high-visibility ML codebases.
- Scientific knowledge of biology, chemistry, or physics along with previous experience working in a scientific environment across disciplines.
Valence Labs is committed to creating a diverse and inclusive environment, where understanding and accommodating personal needs and preferences is a priority. Join our multidisciplinary team of passionate researchers, eager to push the boundaries of ML research and contribute to industrializing scientific discovery to radically improve lives.
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