About the Role
Byteridge is seeking a Data Scientist with 3-5 years of experience in data analysis, statistical modeling, and machine learning to deliver actionable business insights. This role involves translating complex business problems into analytical solutions, building and evaluating ML models, and effectively communicating insights through compelling data stories. The ideal candidate will possess strong statistical foundations combined with hands-on experience across modern data platforms and a collaborative approach to cross-functional teamwork.
What will you need to be successful in this role?
Core Data Science Skills
- Strong foundation in statistics, probability, and mathematical modeling
- Expertise in Python for data analysis (NumPy, Pandas, Scikit-learn, SciPy)
- Strong SQL skills for data extraction, transformation, and complex analytical queries
- Experience with exploratory data analysis (EDA) and statistical hypothesis testing
- Proficiency in data visualization tools (Matplotlib, Seaborn, Plotly, Tableau, or Power BI)
- Strong understanding of feature engineering and data preprocessing techniques
- Experience with A/B testing, experimental design, and causal inference
Machine Learning & Analytics
- Strong experience building and deploying ML models (regression, classification, clustering)
- Knowledge of ensemble methods, gradient boosting (XGBoost, LightGBM, CatBoost)
- Understanding of time series analysis and forecasting techniques
- Experience with model evaluation metrics and cross-validation strategies
- Familiarity with dimensionality reduction techniques (PCA, t-SNE, UMAP)
- Understanding of bias-variance tradeoff and model interpretability
- Experience with hyperparameter tuning and model optimization
GenAI & Advanced Analytics
- Working knowledge of LLMs and their application to business problems
- Experience with prompt engineering for analytical tasks
- Understanding of embeddings and semantic similarity for analytics
- Familiarity with NLP techniques (text classification, sentiment analysis, entity extraction)
- Experience integrating AI/ML models into analytical workflows
Data Platforms & Tools
- Experience with cloud data platforms (Snowflake, Databricks, BigQuery)
- Proficiency in Jupyter notebooks and collaborative development environments
- Familiarity with version control (Git) and collaborative workflows
- Experience working with large datasets and distributed computing (Spark/PySpark)
- Understanding of data warehousing concepts and dimensional modeling
- Experience with cloud platforms (AWS, Azure, or GCP)
Business Acumen & Communication
- Strong ability to translate business problems into analytical frameworks
- Experience presenting complex analytical findings to non-technical stakeholders
- Ability to create compelling data stories and visualizations
- Track record of driving business decisions through data-driven insights
- Experience working with cross-functional teams (Product, Engineering, Business)
- Strong documentation skills for analytical methodologies and findings
Good To Have
- Experience with deep learning frameworks (TensorFlow, PyTorch, Keras)
- Knowledge of reinforcement learning and optimization techniques
- Familiarity with graph analytics and network analysis
- Experience with MLOps and model deployment pipelines
- Understanding of model monitoring and performance tracking in production
- Knowledge of AutoML tools and automated feature engineering
- Experience with real-time analytics and streaming data
- Familiarity with causal ML and uplift modeling
- Publications or contributions to data science community
- Kaggle competitions or open-source contributions
- Experience in specific domains (finance, healthcare, e-commerce)
Competencies
- Excellent analytical and critical thinking skills
- Strong communication skills with ability to explain complex concepts simply
- Intellectual curiosity with passion for solving complex problems
- Self-motivated with ability to work independently on ambiguous problems
- Collaborative mindset with strong team orientation
- Attention to detail with focus on data quality and accuracy
- Continuous learning approach to stay current with data science trends
- Excellent academic record: B.E./B.Tech, M.Tech/MS in Statistics, Mathematics, Computer Science, or related quantitative fields