Senior Data Scientist

Reposted 2 Months Ago
Hiring Remotely in United States
Remote or Hybrid
Senior level
Information Technology • Database • Consulting
The Role
Lead development of time-series and econometric models (ARIMA, VAR, state-space, WLS, panel data) and Bayesian probabilistic frameworks. Use causal inference and stochastic processes to produce actionable insights, work with large datasets on Databricks, communicate results to stakeholders, and mentor junior team members.
Summary Generated by Built In

Key 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.
Responsibilities

Must-Have Qualifications

  • 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)
    • Econometric methods (WLS, regression diagnostics, panel data models)
    • Causal inference (A/B testing, quasi-experimental methods)
    • Bayesian statistics and probabilistic modeling
    • 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.
Qualifications

Secondary / Good-to-Have Skills (General Data Science)

  • 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 

Skills Required

  • Strong foundation in econometrics and time series analysis
  • Hands-on experience with time series models (ARIMA, SARIMA, VAR, forecasting techniques)
  • Experience with econometric methods (WLS, regression diagnostics, panel data models)
  • Experience with causal inference (A/B testing, quasi-experimental methods)
  • Bayesian statistics and probabilistic modeling
  • Experience with Markov chains or stochastic modeling
  • Proficiency in Python
  • Proficiency in 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

EXL Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about EXL and has not been reviewed or approved by EXL.

  • Healthcare Strength — Medical plan options include HSA, HRA, and PPO choices, with additional health-related programs such as disability coverage and an employee assistance program. Feedback suggests these options provide meaningful flexibility and coverage breadth for many roles.
  • Leave & Time Off Breadth — Up to 20 days of paid time off, plus federal holidays and paid sick and safe time, are highlighted. Feedback suggests this leave structure is a notable benefit alongside core compensation.
  • Retirement Support — A traditional and Roth 401(k) with up to a 3% company match and full vesting after one year is described. Feedback suggests this match and vesting timeline help bolster long-term savings.

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The Company
HQ: New York, NY
30,246 Employees
Year Founded: 1999

What We Do

Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.

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