OUR HIRING PROCESS:

• We will review your application against our job requirements. We do not employ machine learning technologies during this phase as we believe every human deserves attention from another human. We do not think machines can evaluate your application quite like our seasoned recruiting professionals—every person is unique. We promise to give your candidacy a fair and detailed assessment.
• We may then invite you to submit a video interview for the review of the hiring manager. This video interview is often followed by a test or short project that allows us to determine whether you will be a good fit for the team.
• At this point, we will invite you to interview with our hiring manager and/or the interview team. Please note: We do not conduct interviews via text message, Telegram, etc. and we never hire anyone into our organization without having met you face-to-face (or via Zoom). You will be invited to come to a live meeting or Zoom, where you will meet our INFUSE team.
• From there on, it’s decision time! If you are still excited to join INFUSE and we like you as much, we will have a conversation about your offer. We do not make offers without giving you the opportunity to speak with us live. After all, we consider our team members our family, and we want you to feel comfortable and welcomed.INFUSE is committed to complying with applicable data privacy and security laws and regulations. For more information, please see our Privacy Policy
  We are seeking a talented Data Scientist to join our Research and Development (R&D) team. In this role, you will be responsible for developing analytical solutions for complex problems that cannot be solved using simple algorithms or traditional code. You will work on a variety of cutting-edge projects, including data-driven insights, predictive modeling, machine learning, and optimization. This is an exciting opportunity to apply your skills in a dynamic R&D environment and contribute to the development of next-generation solutions.

KEY QUALIFICATIONS:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or a related field.
• 3+ years of experience as a Data Scientist, with a strong focus on machine learning, statistical modeling, and data analysis.
• Strong knowledge of data science techniques, including regression, clustering, classification, and time-series analysis.
• Expertise in machine learning libraries and frameworks (e.g., TensorFlow, Scikit-learn, Keras, PyTorch).
• Proficiency in programming languages such as Python, R, or similar, and experience with data manipulation and analysis libraries (e.g., Pandas, NumPy).
• Familiarity with big data tools and platforms (e.g., Hadoop, Spark) is a plus.
• Solid understanding of data visualization tools (e.g., Matplotlib, Tableau, PowerBI) to communicate insights effectively.
• Strong analytical and problem-solving skills, with the ability to work with large and complex datasets.
• Excellent communication skills, with the ability to explain complex data findings to non-technical stakeholders.
• Ability to work independently, manage projects, and collaborate with cross-functional teams.

• Defining Problem-Solving Approaches:
• Analyze business problems, formulate hypotheses, and propose methods for testing them.
• Develop and test technologies for data analysis and decision-making automation.
• Data Management:
• Organize data collection, cleaning, and structuring for further analysis.
• Define necessary data requirements and collaborate with the team on data preparation.
• Model Development & Machine Learning:
• Design, build, and validate machine learning models to address business problems and improve product performance.
• Develop predictive models, recommendation systems, and classification algorithms to solve complex challenges.
• Optimize models, test them on real-world data, and assess the quality of solutions.
• Reporting & Solution Integration:
• Primary task: Automate processes, including data collection and processing, optimization of existing processes, and acceleration of system performance.
• Prepare reports with analysis results, including metrics to evaluate hypothesis effectiveness.
• Work closely with product managers and business analysts, helping them understand potential limitations and risks during implementation.
• Automation & AI Models:
• Use generative and specialized neural networks to automate processes.
• Optimize AI model prompts for better interaction.
• Seek pre-built solutions for accelerating workflows while ensuring deep understanding of neural network fine-tuning.
• Data-Driven Research & Innovation:
• Collaborate with the R&D team to explore new research areas and apply data science techniques to support innovative product development.
• Conduct experiments and hypothesis testing to validate assumptions and optimize models.
• Collaboration & Cross-Functional Work:
• Work closely with product managers, engineers, and other team members to align data-driven solutions with business goals.
• Contribute to team knowledge-sharing, ensuring that data science best practices are adopted across the organization.
• Continuous Learning & Innovation:
• Stay up-to-date with the latest advancements in data science, machine learning, and artificial intelligence.
• Integrate new techniques into R&D projects and experiment with new tools and technologies.