Kick-start your machine learning career with a 3‑month remote, hands‑on training program designed for fresh graduates to turn Python skills into real AI expertise.
Responsibilities
Work on production‑grade generative AI projects
Learn the exact skills companies are hiring for today
Build and deploy a research assistant system
Fine‑tune models, multi‑modal agents, and real‑time inference
Compensation is competitive for the sustainability sector.
Responsibilities
Identify, design, and drive interventions that improve outcomes for smallholder farmers and their communities
Research, analyze, and quantify the potential of solutions to sustainability challenges
Assess and develop candidate interventions and ideas across precision agriculture, sustainable livelihoods, carbon sequestration, microfinance, and related areas
Lead the design, launch, and management of programs—piloting, partnering, monitoring, evaluating, and scaling what works
Spend substantial time at origin, in smallholder communities across Sub-Saharan Africa, Latin America, or Asia
Lead R&D work in artificial intelligence for formal verification within the VITAMIN-C project.
Compensation
Indicative remuneration range (excluding the variable annual bonus): €33,614 – €43,000 gross per annum, depending on profile and experience, as defined by our legal framework.
Responsibilities
Carry out R&D work in artificial intelligence for formal verification
Contribute to the research profile of Télécom Paris and Institut Mines-Télécom
Requirements
Bac+5 or equivalent
Bac+4 with at least four years’ professional experience
Quantitative Risk Analyst in the Private Wealth Investment Risk team.
Compensation
The salary range for this role is $95,000-$110,000.
Responsibilities
Work as an integral part of the Private Wealth Investment Risk team, taking up day-to-day coverage of several of the strategies/portfolio management teams
Maintain and run regular and ad hoc reports; provide support to various teams on risk and performance analytics issues
Translate academic and/or buy-side research into risk models, analytics, and methodologies that are implementable and actionable
Perform ex-ante and ex-post portfolio risk analysis, performance and attribution analysis, stress tests, and back tests
Stay connected and current with academic finance research and developments and present findings to team members
Requirements
Bachelor’s or master’s Degree in a quantitative field like Financial Engineering, Econometrics, Computer Science, Applied Mathematics, Statistics, or similar
Design, develop, validate, and maintain workforce models and simulation capabilities to evaluate force structure changes, workforce flows, training pipelines, and readiness impacts, supporting workforce optimization and leadership decision-making.
Compensation
W2 Hourly rate: $45-$55. Pay rates are based on experience, qualifications, location, and certifications, and are subject to change at any time.
Responsibilities
Analyze, integrate, and prepare workforce, training, readiness, and force structure data from sources such as Envision, STARS DMOG, AFMA, ARM, LCOM, and AETC systems.
Develop and maintain workforce flow models, net flow analyses, and workforce health assessments to identify key drivers, bottlenecks, and constraints across the 2A force generation enterprise.
Design, develop, calibrate, and validate simulation models, including Discrete Event Simulation and System Dynamics, to evaluate workforce planning scenarios and force structure alternatives.
Apply quantitative analysis and forecasting techniques to assess workforce trends and readiness impacts.
Develop visualizations, dashboards, technical documentation, and analytical products to communicate findings and support leadership decision-making.
Design, develop, test, deploy, and maintain scalable data pipelines using Databricks and related technologies.
Responsibilities
Design, develop, test, deploy, optimize, and support complex, scalable, production-grade data pipelines using Databricks and related data engineering technologies.
Develop robust ETL/ELT solutions to ingest, transform, integrate, and deliver data across cloud and enterprise environments.
Design reusable and maintainable data integration patterns leveraging Databricks, Apache Spark/PySpark, Python, SQL, Delta Lake, APIs, and other appropriate technologies.
Design and maintain efficient data models, data structures, and storage solutions.
Implement data quality, validation, reconciliation, monitoring, and error-handling processes to ensure reliable and accurate data delivery.
Requirements
Bachelor's degree in computer science, Information Systems, Engineering, Data Engineering, or a related field, or equivalent professional experience
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