About Catalytics Datum
Catalytics Datum is the Next-Gen Enterprise that amalgamates Data Science, Big Data, Cloud Computing & Business Intelligence to solve complex business problems for enterprises through user experience and faster decision-making. Recognized by Microsoft BizSpark, Catalytics is present across the globe to become your partner in Digital Transformation.
Catalytics Datum offers Platform as a Service, which is One Stop Solution. The complete process; starting from Requirement Gathering to the Final Deployment, is data-driven, processed by collaborative and different Predictive modeling tools which leave clients overhead free. We provide up to 99.9% accurate results in order to increase profitability by providing the deepest insights of your brands.
Role Summary
We are looking for a Data Scientist (2–4 years experience) to work on building, validating, and deploying machine learning models that drive measurable business outcomes. You will partner with product, engineering, and business teams to translate real-world problems into data-driven solutions, and clearly communicate model insights and impact.
Key Responsibilities
- Build and validate ML models for business use cases (classification, regression, clustering, recommendations)
- Perform data wrangling, feature engineering, and exploratory data analysis (EDA)
- Partner with product and business teams to translate problems into data science solutions
- Develop dashboards and visualizations to communicate insights and model performance
- Participate in the ML lifecycle – training, validation, deployment, and monitoring
- Support model explainability and communicate trade-offs and limitations clearly
- Collaborate with engineering teams for production deployment of models
- Contribute to documentation, reusable notebooks, and best practices
Minimum Qualifications
- 2–4 years of hands-on experience in Data Science or Applied ML
- Strong proficiency in Python (or R) for data analysis and modeling
- Solid grounding in statistics and machine learning fundamentals
- Experience working with SQL and real-world datasets
- Exposure to deploying ML models into production or staging environments
- Ability to present insights and model outcomes to non-technical stakeholders
Nice-to-Have Qualifications
- Experience with big data tools (Spark, Databricks)
- Exposure to deep learning frameworks (TensorFlow, PyTorch)
- Familiarity with MLOps tools and CI/CD for ML
- Experience across multiple business domains
- Experience presenting to leadership or client stakeholders
What Success Looks Like (First 6–12 Months)
- Delivered 1–2 production ML models with measurable business impact
- Built reliable data pipelines and reusable feature sets
- Improved model performance through structured experimentation
- Created clear dashboards and narratives explaining model impact
- Earned trust from stakeholders through strong communication and delivery
