About the Role

We are seeking candidates with a strong background in Python, deep applied expertise in one or more data science specialties (e.g., machine learning, LLMs/GenAI, optimization, geospatial analytics, MLOps, or full-stack engineering for data products), experience building and deploying solutions in production, and a passion for building solutions to difficult social problems. Most importantly, successful candidates should have the ability to learn and adapt quickly, and work independently to solve complex human and technological challenges.

As a senior data scientist, you'll lead multiple projects as tech lead and be responsible for the performance of the solutions. Day-to-day work may include:

• Working with clients to understand their needs: Understanding their current processes and pain points, identifying which of these can be framed as tractable data science problems, and knowing when they can't, and even when it is, the solution must suit the task and resources available.
• Leading solution design and delivery end-to-end: Rolling up your sleeves as an individual contributor- writing production code, designing and evaluating methods and models, and building and deploying solutions (including APIs, UIs, CI/CD, etc.); while also working alongside other data scientists to shape the overall approach, synthesize findings, and communicate results.
• Establishing standards and best practices: Bringing experience in writing quality code, conducting code reviews, and providing feedback on technical and non-technical documentation. Setting the bar for engineering rigor and project management on the team.
• Coaching and mentoring: Upskilling junior data scientists on methods and approaches, and providing structured feedback on both technical and delivery skills.
• Providing thought leadership in your specialty: Leading the org through deep expertise in a subset of the following (machine learning, deep learning, GenAI/LLMs, NLP, optimization, geospatial analytics, backend development, frontend development, DevOps, or MLOps) — and drawing on broad familiarity across methods to make sound judgment calls on which approach fits which problem.
• Shaping the sector: Identifying trends and gaps in the AI-for-Good space; proposing products or services that could address these gaps; and contributing thought leadership on how data science and AI can be deployed responsibly and for the right problem types.

Moreover, professional development for our technical roles is essential for IDinsight’s long-term impact. With support from IDinsight leadership, the employee will maintain self-directed professional development plans and will be given "stretch" opportunities designed to strengthen their professional skills. Real-time feedback and structured reviews are regularly provided to maximize each data scientist’s expertise. IDinsight’s entrepreneurial culture allows roles and career progression to be tailored to individual strengths, interests, and goals. Employees have the opportunity to increase responsibilities, and high performers will have the opportunity to move up in the organization along technical, managerial, or client-facing paths.

Required Technical Qualifications

Master's degree and 8 years of experience, or a PhD and 4 years of experience, as a data scientist working in Python.

Demonstrated expertise in a subset of the following data science specialties: predictive modelling, machine learning, deep learning, GenAI/LLMs, NLP, optimization, or geospatial analytics.

Intermediate-to-advanced Python skills / experience working on complex codebases.

Working knowledge of at least one of: AI engineering, MLOps, backend development, DevOps, and frontend development

Broad knowledge of advanced machine learning and data science methods.

Strong foundations in statistics and probability.

Proficiency in collaborative software development practices such as version control and code reviews

Other required qualifications:

Proven ability to work independently and with teams in a dynamic, multicultural environment.

Experience leading technical teams to deliver complex data science or AI solutions, with a strong interest in mentoring, knowledge-sharing, presenting work and providing feedback to others.

Strong oral and written communication skills in English. Professional proficiency in French (written and spoken) is a plus.

Self-starter who will thrive while tackling new, unusual and unpredictable challenges.

Deeply passionate about global development and improving lives in disadvantaged populations.

• Working with clients to understand their needs: Understanding their current processes and pain points, identifying which of these can be framed as tractable data science problems, and knowing when they can't, and even when it is, the solution must suit the task and resources available.
• Leading solution design and delivery end-to-end: Rolling up your sleeves as an individual contributor- writing production code, designing and evaluating methods and models, and building and deploying solutions (including APIs, UIs, CI/CD, etc.); while also working alongside other data scientists to shape the overall approach, synthesize findings, and communicate results.
• Establishing standards and best practices: Bringing experience in writing quality code, conducting code reviews, and providing feedback on technical and non-technical documentation. Setting the bar for engineering rigor and project management on the team.
• Coaching and mentoring: Upskilling junior data scientists on methods and approaches, and providing structured feedback on both technical and delivery skills.
• Providing thought leadership in your specialty: Leading the org through deep expertise in a subset of the following (machine learning, deep learning, GenAI/LLMs, NLP, optimization, geospatial analytics, backend development, frontend development, DevOps, or MLOps) — and drawing on broad familiarity across methods to make sound judgment calls on which approach fits which problem.
• Shaping the sector: Identifying trends and gaps in the AI-for-Good space; proposing products or services that could address these gaps; and contributing thought leadership on how data science and AI can be deployed responsibly and for the right problem types.

• Python
• Machine learning
• LLMs/GenAI
• Optimization
• Geospatial analytics
• MLOps
• Full-stack engineering for data products
• Deep learning
• NLP
• Backend development
• Frontend development
• DevOps
• AI engineering
• Statistics
• Probability
• Version control
• Code reviews

• Master's degree and 8 years of experience, or a PhD and 4 years of experience, as a data scientist working in Python.
• Demonstrated expertise in a subset of the following data science specialties: predictive modelling, machine learning, deep learning, GenAI/LLMs, NLP, optimization, or geospatial analytics.
• Intermediate-to-advanced Python skills / experience working on complex codebases.
• Working knowledge of at least one of: AI engineering, MLOps, backend development, DevOps, and frontend development
• Broad knowledge of advanced machine learning and data science methods.
• Strong foundations in statistics and probability.
• Proficiency in collaborative software development practices such as version control and code reviews
• Proven ability to work independently and with teams in a dynamic, multicultural environment.
• Experience leading technical teams to deliver complex data science or AI solutions, with a strong interest in mentoring, knowledge-sharing, presenting work and providing feedback to others.
• Strong oral and written communication skills in English. Professional proficiency in French (written and spoken) is a plus.
• Self-starter who will thrive while tackling new, unusual and unpredictable challenges.
• Deeply passionate about global development and improving lives in disadvantaged populations.