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Key Responsibilities and Required Skills for Insight Consultant

💰 $ - $

🎯 Role Definition

The Insight Consultant is a data-driven business partner responsible for turning complex data into clear, actionable recommendations that drive strategic decisions and commercial outcomes. This role bridges analytics, market research, and stakeholder management: conducting rigorous quantitative and qualitative analysis, building scalable dashboards and models, and communicating findings to senior leaders in a way that influences product, marketing, pricing, and operational strategy. The ideal candidate combines technical fluency (SQL, Python/R, visualization tools) with commercial instincts and exceptional storytelling to translate data into measurable impact.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst or Business Analyst transitioning into strategic insight delivery.
  • Market Research Analyst or Consumer Insights Specialist with strong quantitative skills.
  • Product or Marketing Analyst responsible for performance measurement and reporting.

Advancement To:

  • Senior Insight Consultant / Lead Insight Consultant
  • Analytics Manager or Head of Insights
  • Director of Strategy, Product Analytics, or Commercial Insights

Lateral Moves:

  • Product Analytics / Growth Analytics
  • Data Science or Machine Learning Engineer
  • Strategy and Corporate Development roles

Core Responsibilities

Primary Functions

  • Lead end-to-end insight projects by partnering with business stakeholders to define the problem statement, identify relevant data sources, design the analytical approach, and deliver prioritized recommendations that align with commercial objectives and KPIs.
  • Design, build and maintain robust SQL queries, ETL routines, and reproducible analysis pipelines to extract, clean, and transform large customer, product, and transaction datasets for insight generation.
  • Develop predictive models, forecasting tools, and propensity models (using Python, R, or similar) to anticipate customer behavior, inform segmentation, and quantify potential revenue or retention impacts.
  • Create high-impact, executive-level dashboards and visualizations in Tableau, Power BI, Looker, or equivalent platforms that surface key metrics, trends, and signals for real-time decision making.
  • Conduct hypothesis-driven experimentation and A/B test design and analysis — from sample sizing and randomization checks to statistical inference and post-test causal interpretation — to validate product and marketing changes.
  • Translate complex statistical results into concise, non-technical business narratives and actionable recommendations that drive prioritization and investment decisions across marketing, product, and operations teams.
  • Lead customer segmentation and cohort analyses to identify high-value audiences, lifecycle behaviors, and churn risk, and recommend targeted interventions to improve acquisition, engagement, and lifetime value.
  • Quantify the financial impact and ROI of strategic initiatives by building business cases, scenario analyses, and sensitivity models that forecast revenue, cost, and margin implications.
  • Perform competitive benchmarking and market landscape assessments combining internal metrics with external data sources to identify strategic opportunities and threats.
  • Partner with data engineering and product teams to operationalize insights into production systems, ensuring models and dashboards are reliable, scalable, and maintainable.
  • Manage multi-stakeholder workshops and discovery sessions to gather business requirements, align on objectives, and co-create measurement frameworks and success criteria.
  • Implement and maintain measurement frameworks and KPI taxonomies across channels and products to ensure consistent reporting, governance, and performance tracking.
  • Lead deep-dive root cause analyses for significant performance shifts, synthesizing quantitative signals with qualitative inputs (surveys, user research) to determine corrective actions.
  • Drive adoption of analytics outputs by designing stakeholder training, documentation, and self-service reporting that empower teams to use insights independently.
  • Prioritize analytics backlog and govern resource allocation by assessing business value, analytic complexity, and time-to-impact for proposed insight projects.
  • Communicate findings and influence outcomes through polished slide decks, one-pagers, and live presentations to cross-functional teams and executive leadership, tailoring content for different audiences.
  • Ensure analytical rigor and reproducibility by implementing version control, code review, and standardized modeling practices across insight projects.
  • Support pricing strategy and elasticity analysis by modeling demand sensitivity and recommending price tests and segmentation-based pricing strategies.
  • Conduct ad-hoc and scenario analyses for M&A diligence, new market entry, or product launch planning, synthesizing multiple data sources into a coherent assessment of opportunity and risk.
  • Collaborate with marketing and analytics teams to instrument tracking, improve data quality, and define event taxonomies that enable more accurate measurement and attribution.
  • Monitor and report on industry and customer trend signals, alerting the business to emerging opportunities and risks by establishing regular insights cadences and watchlists.
  • Mentor junior analysts and insight practitioners by providing technical guidance, constructive feedback, and career development support to build a high-performing insights capability.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL: ability to write complex, performant queries, optimize joins, window functions, and aggregate logic on large datasets.
  • Data manipulation and programming: proficiency in Python (pandas, scikit-learn) or R for data cleaning, statistical modeling, and automation.
  • Data visualization: strong experience creating interactive dashboards and executive reports using Tableau, Power BI, Looker, or similar tools.
  • Statistical analysis and experimentation: knowledge of hypothesis testing, A/B testing methodology, confidence intervals, power calculations, and uplift modeling.
  • Predictive modeling and machine learning fundamentals: regression, classification, time-series forecasting, and model evaluation techniques.
  • ETL and data pipeline awareness: familiarity with data ingestion, transformation workflows, and tools such as Airflow, dbt, or cloud-native services.
  • Cloud data platforms: experience with Snowflake, BigQuery, Redshift, or equivalent data warehousing solutions.
  • Measurement and analytics frameworks: attribution modeling, funnel analysis, cohort analysis, and customer lifetime value (LTV) modeling.
  • Business case and financial modeling: ability to convert analytics outputs into revenue and cost impact assessments and scenario planning.
  • Web and product analytics: experience with Google Analytics, Mixpanel, Amplitude, or other event-tracking systems for behavior analysis.
  • SQL-based BI tooling and LookML/Looker or similar semantic layer experience (preferred).
  • Data governance and quality controls: understanding of lineage, metadata, and best-practices for trustworthy analytics.

Soft Skills

  • Business acumen: strong commercial instincts and the ability to connect analytics to strategic business outcomes and KPIs.
  • Storytelling and communication: exceptional ability to present complex analyses clearly and persuasively to non-technical stakeholders and executives.
  • Stakeholder management: adept at building trust, negotiating scope, and influencing cross-functional partners without formal authority.
  • Problem solving and curiosity: rigorous, hypothesis-driven approach to break down ambiguous problems and surface root causes.
  • Project management: ability to manage multiple concurrent initiatives, prioritise backlog, and deliver on time with high quality.
  • Collaboration and coaching: supportive team player who mentors junior analysts and fosters a culture of continuous improvement.
  • Adaptability and learning mindset: comfortable operating in fast-changing environments and upskilling in new tools and methodologies.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in a quantitative or business-related discipline (e.g., Statistics, Economics, Mathematics, Computer Science, Business Analytics).

Preferred Education:

  • Master’s degree in Data Science, Analytics, Business Analytics, Economics, Statistics, or an MBA with a quantitative focus.

Relevant Fields of Study:

  • Data Science / Analytics
  • Statistics / Mathematics
  • Economics
  • Business / Finance / Marketing
  • Computer Science

Experience Requirements

Typical Experience Range:

  • 3–7 years of progressive experience in analytics, insights, market research, or related roles supporting product, marketing, or commercial teams.

Preferred:

  • 5+ years of experience delivering end-to-end insights programs in consumer tech, e-commerce, financial services, consulting, or media organizations, with demonstrable impact on revenue, retention, or product strategy.
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