Senior Data Scientist

As a Senior Data Scientist at Acumatica, your main responsibilities will include optimizing and deploying language models for ERP tasks, designing AI agents to handle workflows, and developing traditional machine learning models to tackle business problems. You'll collaborate with various teams to define AI use cases and take ownership of the MLOps lifecycle, ensuring the integration of models into production systems is seamless.

To qualify for the Senior Data Scientist role at Acumatica, you should have over 5 years of hands-on experience in data science, focusing on machine learning and natural language processing. Proficiency in Python and various ML libraries, experience with model deployment on cloud infrastructure, and familiarity with AI agents and MLOps best practices are crucial. Strong communication skills and collaboration experience are also important.

Absolutely! The Senior Data Scientist position at Acumatica emphasizes collaboration across multiple teams. You will work closely with product managers, software engineers, and UX designers to define AI use cases and ensure that the models you create are seamlessly integrated into production systems, driving innovation and efficiency.

In your role as a Senior Data Scientist at Acumatica, you'll work with a variety of tools and technologies, including Python, TensorFlow, PyTorch, Scikit-learn, and HuggingFace for machine learning and natural language processing. Additionally, experience with frameworks like LangChain and AutoGPT will be beneficial as you design AI solutions for our cloud infrastructure.

Working as a Senior Data Scientist at Acumatica in Serbia comes with a range of benefits, including a generous relocation package, private health insurance, numerous vacation days, performance bonuses, and excellent opportunities for career growth. We believe in supporting our team's well-being and development throughout their journey with us.

When answering this question, highlight specific projects where you've successfully fine-tuned language models, the challenges faced, and the outcomes achieved. Be sure to mention the tools and techniques used, as well as how your work contributed to business goals or enhanced functionality in previous roles.

Discuss your hands-on experience with various time series forecasting algorithms, such as ARIMA, Exponential Smoothing, or LSTM networks. Provide examples of projects where you've implemented these techniques and how you handled challenges like seasonality, trends, and cycles within the data.

Emphasize your commitment to ethical AI practices by discussing how you evaluate bias in data, ensure transparency in model decisions, and strive for fairness in your algorithms. Share any frameworks or guidelines you follow to maintain ethical standards during model design and deployment.

In your answer, provide detailed examples of how you've implemented MLOps practices, including setting up CI/CD pipelines for machine learning models. Discuss the tools used, such as Git for version control or Jenkins for automation, and the processes you put in place for model monitoring and retraining.

Explain your approach to feature engineering, including how you identify important features, handle missing data, and create new features that can improve model performance. Highlight specific projects where your feature engineering efforts led to significant improvements in model effectiveness.

Mention your familiarity with AI frameworks such as LangChain and AutoGPT. Discuss specific use cases where you've employed these frameworks and what led you to choose them for your projects. Provide examples of the outcomes achieved through your implementations.

Talk about your methodology for evaluating models, including the selection of performance metrics based on project requirements. Highlight your experience with techniques like A/B testing and cross-validation to ensure solid performance while reducing overfitting.

Share a specific example of a project where collaboration with other teams—such as product managers or UX designers—led to significant improvements in project outcomes. Highlight the importance of clear communication and joint problem-solving during the project.

Discuss your specific experience deploying models on cloud platforms like Azure or AWS. Describe the deployment process you followed, how you handled scalability and security concerns, and any challenges you encountered during the deployment phase.

Explain your strategies for ongoing model monitoring, including how you track performance, detect drifts, and determine when to retrain models. Provide insights into the monitoring tools and processes that you find effective in maintaining model integrity post-deployment.