Job description

Description & Requirements How You Will Contribute: • Engage with clients professionally and consistently to understand their needs, deliver effective solutions, and build trust. • Assess clients' data strategy, systems, and datasets by exploring cloud and on-premise infrastructure and conducting meetings. • Contribute to the design and development of data products, leading aspects of technical design. • Conduct exploratory data analysis, develop predictive models, and create pipelines that generate reliable outputs for visualizations and applications. • Set a high standard for quality and performance in code development, ensuring integration with CI pipelines through functional and unit testing. • Take on leadership responsibilities in client projects as technical expertise grows. • Provide training and mentorship to team members and interns as needed. • Lead and support client engagement throughout all phases of the project lifecycle. We are looking for people who have Forward Vision and: • Strong problem-solving abilities, particularly in product development. • Motivation to drive business impact through data-driven strategies. • Comfort working with diverse stakeholders and teams. • Excellent written and verbal communication for cross-team collaboration. • Enthusiasm for learning and mastering new technologies in a fast-evolving environment. • Ability to travel occasionally • Ability to support and engage with users of data and analytics products. Minimum Qualifications • Bachelor's degree in a Data & Analytics-related field (or equivalent experience), such as Data Science or Data Engineering. • Knowledge of cloud architecture, system design, and integration. • Expertise in developing and deploying data models and products in production environments. • Proficiency in data visualization tools such as PowerBI or Tableau. • Experience with text mining, NLP, or Generative AI. • Background in solution architecture, DevOps, or data engineering with platforms like Azure, AWS, or GCP. • Strong proficiency in Python, Pandas, Pyspark, and scikit-learn. • Understanding of various machine learning techniques, including clustering, decision trees, boosting, and neural networks. • Familiarity with Linux ecosystems, including Bash, SSH, virtualization, and automation. • Experience with databases such as SQL and Spark-based platforms (Databricks, Fabric, Synapse). • Comfort working with Git for Continuous Integration and Deployment (GitLab CI, Docker Compose). • Strong command of data exploration and analysis in an open-source environment. • Proven ability to develop and implement machine learning models and simulations. • Experience in data visualization (preferred).

Requirements