Note: The job is a remote job and is open to candidates in USA. EXL is a company focused on digital solutions, and they are seeking a Senior Data Scientist to lead the development of advanced forecasting models and apply econometric techniques to solve complex business problems.
Responsibilities
- Lead development of time series forecasting models (ARIMA, VAR, state-space models, etc.) for business-critical use cases
- Apply econometric techniques such as WLS, panel data models, and causal inference methods to solve real-world business problems
- Design and implement Bayesian models and probabilistic frameworks for uncertainty estimation and decision-making
- Utilize Markov chains and stochastic processes for modeling sequential or behavioral data
- Translate business problems into robust analytical frameworks and deliver actionable insights
- Work with large datasets using Databricks
- Collaborate with stakeholders across business and technical teams to ensure model relevance and impact
- Mentor junior team members and drive best practices in statistical modeling and experimentation
Skills
- Strong foundation in econometrics and time series analysis (this is critical for the role)
- Hands-on experience with time series models (ARIMA, SARIMA, VAR, forecasting techniques)
- Hands-on experience with econometric methods (WLS, regression diagnostics, panel data models)
- Hands-on experience with causal inference (A/B testing, quasi-experimental methods)
- Hands-on experience with Bayesian statistics and probabilistic modeling
- Hands-on experience with Markov chains or stochastic modeling
- Proficiency in Python along with SQL
- Experience working with Databricks or similar big data platforms
- Ability to clearly communicate complex statistical concepts to non-technical stakeholders
- Experience with machine learning models (classification, regression, tree-based models, etc.)
- Familiarity with feature engineering, model validation, and performance tuning
- Exposure to ML pipelines and MLOps concepts
Company Overview
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