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Salary
≈ $23k – $51k per year (Estimated)
Location
Hybrid (Bengaluru, India)
Seniority
Senior · 5+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 19, 2026. First seen by Alion on Sep 21, 2026. Symphoni HR scores A on the Alion truth index.

Overview
Company
Impact
Profile match
Symphoni HR is an executive search and specialist recruitment firm headquartered in Malad West, Mumbai, handling board, CEO and CXO appointments alongside technology hiring, recruitment process outsourcing, project hiring and fractional leadership services. Established in 2003, it reports more than 100,000 candidates hired and over 5,000 C-suite and senior mandates for 300+ clients across practices such as global capability centres, financial services, technology, industrial and healthcare. The roles on its board are client vacancies, mostly in Mumbai, Bengaluru and Pune, for Java and C++ developers, data engineers, DevOps and GenAI engineers, business analysts and project managers.

We are seeking a Causal Machine Learning Scientist to join a high-impact team in Bengaluru. In this hybrid role, you will design and implement sophisticated causal inference models to measure the impact of business interventions and optimize decision-making processes. You will bridge the gap between complex observational data and actionable business insights using cutting-edge statistical and machine learning techniques.

Our client is a leading global professional services firm specializing in accounting and financial advisory. They are committed to leveraging advanced data science and artificial intelligence to drive strategic decision-making for their diverse portfolio of international clients. The organization fosters a culture of innovation, analytical rigor, and professional growth.

Responsibilities

  • Design and analyze end-to-end controlled experiments including A/B tests and weblabs.
  • Perform power and sample-size analysis to ensure robust experimental outcomes.
  • Develop causal graphs to identify confounders and mediators for valid effect estimation.
  • Implement observational estimators such as Double/Debiased ML, Propensity Score Matching, and Inverse Propensity Weighting.
  • Build and maintain scalable causal pipelines from raw data to defensible effect estimates.
  • Translate complex causal findings into clear, actionable recommendations for non-technical stakeholders.
  • Integrate LLM agents into the causal analysis loop to assist with hypothesis generation and estimator selection.
  • Collaborate with cross-functional teams to deploy solutions within an AWS-based data environment.

Requirements

  • 5-7 years of professional experience in data science or machine learning.
  • Proficiency in Python and SQL for data manipulation and model development.
  • Deep expertise in causal inference methodologies including synthetic control and difference-in-differences.
  • Hands-on experience with causal libraries such as DoWhy, EconML, and CausalPy.
  • Strong understanding of AWS data and analytics services including Redshift, S3, Lambda, and ECS.
  • Proven ability to communicate uncertainty and assumptions effectively to business leaders.
  • Experience working in a hybrid environment based in Bengaluru.
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