(JD)

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JOB TITLE | D2C Analytics & Insights – DM/M |

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GRADE / LEVEL | DM / AGM |

(ENTRY AND EXIT GRADE) | |

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DEPARTMENT/FUNCTION | E-COMMERCE |

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REPORTS TO | Business Head - D2C |

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LOCATION | Bangalore |

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EMPLOYMENT TYPE | Full-time |

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JOB PURPOSE | We are looking for a highly analytical |

and business-oriented professional to |

build the D2C Analytics & Business |

Intelligence function. |

This role will be responsible for |

transforming data from multiple digital, |

customer, marketing, technology and |

operational systems into actionable |

business insights and recommendations. |

The role goes significantly beyond |

traditional MIS and reporting. |

The ideal candidate should be someone who |

can look at a business problem, identify |

the right data required to diagnose it, |

connect information across multiple |

systems, identify the underlying issue |

and translate the findings into clear |

actions for business, product, marketing, |

technology and operations teams. |

This is an individual contributor role |

with high business ownership and |

cross-functional influence. |

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KEY RESPONSIBILITIES | Business Analytics & Decision Making |

• Work closely with the D2C stakeholders |

to identify business questions that can |

be answered through data. |

• Go beyond reporting "what happened" to |

explain: |

- What happened? |

- Why did it happen? |

- Where is the opportunity/problem? |

- What should we do about it? |

- What will be the expected business |

impact? |

• Develop recurring analytical frameworks |

covering revenue, customers, products, |

marketing, digital funnel and operations. |

• Proactively identify business |

opportunities, leakages and anomalies |

rather than waiting for stakeholders to |

ask for reports. |

• Convert complex analysis into concise, |

decision-oriented recommendations for all |

stakeholders. |

• Identify high-value customer segments |

and opportunities to improve purchase |

frequency, retention and customer |

lifetime value. |

• Identify behavioral differences between |

website, app and offline customers. |

• Develop a unified understanding of |

customers across online and offline |

touchpoints. |

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E-commerce & Product Analytics |

• Own analytical understanding of the |

complete digital funnel: |

Traffic → Landing → Browse → Search → PLP |

→ PDP → Add to Cart → Checkout → Payment |

→ Order → Delivery → Repeat Purchase |

• Monitor and analyze funnel performance |

across website and app. |

• Identify conversion leakages and their |

root causes. |

• Analyze: |





- Search behavior |

- Product discovery |

- Product engagement |

- Add-to-cart |

- Checkout |

- Payment |

- Cancellation |

- Return |

- Delivery |

- NPS |

- Ticket to Order Ratio |

• Identify relationships between product |

availability, pricing, MPs discounting, |

content presentability, traffic source |

and conversion. |

• Partner with product and technology |

teams to identify opportunities for UX, |

CRO and product improvements. |

• Measure impact of new product (NPD |

styles) launches, discontinuing of |

existing styles, tech feature releases |

and marketing experiments. |

• Evaluate whether marketing channels are |

generating incremental customers and |

revenue. |

• Identify opportunities for better |

budget allocation using customer and |

business-level data. |

• Build dashboards for one point view of |

business, customer behavior, product & |

tech, marketing, operations. Dashboard |

should be accompanied by insights, |

anomalies, trends and recommended actions |

wherever relevant. |

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AI & Analytics Automation |

• Identify opportunities to use AI to |

improve both analytics productivity and |

business decision-making. |

• Potential areas include: |

- Automated insight generation |

- Natural-language querying of business |

data |

- AI-powered anomaly detection |

- Automated business reviews |

- Customer propensity models |

- Personalized recommendations |

- Marketing optimization |

- Automated root-cause analysis |

- Analytics workflow automation |

• The role should continuously explore |

how AI can reduce manual reporting and |

increase the speed and quality of |

decision-making. |

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Cross-Functional Problem Solving |

• The role will work closely with: |

- D2C Business Head |

- Product & technology team |

- UX/UI team |

- Marketing team |

- CRM & loyalty team |

- Operations team |

- Retail planning team |

- Customer care & finance team |

• The individual is expected to challenge |

assumptions with data and identify issues |

outside the immediate analytics function. |

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What This Role Is NOT |

• This is not a traditional MIS/reporting |

role. |

• The successful candidate should not |

spend most of their time: |

- Preparing static Excel reports |

- Copy-pasting numbers between systems |

- Building dashboards without |

recommendations |

- Waiting for business teams to ask |

questions |

- Reporting metrics without |

understanding their drivers |

• Instead, the role should continuously |

ask: |

"What is the data telling us that the |

business currently doesn't know?" |





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PERFORMANCE METRICS | The person will be expected to deliver: |

Weekly |

- D2C business performance insights |

- Funnel and customer diagnostics |

- Key anomalies and opportunities |

- Marketing/business performance |

insights |

Monthly |

- Customer cohort and retention |

analysis |

- LTV and acquisition quality analysis |

- Product and funnel performance |

- Cross-functional business insights |

Quarterly |

- Deep-dive strategic analyses |

- Growth opportunity identification |

- Customer behavior studies |

- Measurement/attribution improvements |

- Advanced analytics use cases |

Ongoing |

- Data quality improvement |

- Dashboard automation |

- Measurement framework improvement |

- Recent analytical models |

- AI/automation initiatives |

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REQUIRED QUALIFICATIONS | Bachelor's Degree in Data Science or Data |

engineering preferred. MBA preferred. |

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EXPERIENCE | • 5–9 years of experience in one or more |

of the below: |

- Business Analytics |

- Product Analytics |

- Customer Analytics |

- E-commerce Analytics |

- Growth Analytics |

- Marketing Analytics |

- Business Intelligence |

• Experience in e-commerce, D2C, retail, |

consumer internet, marketplace, FMCG or |

consumer technology. Apparel/FMCG |

experience preferred. |

• Experience working directly with |

business/product/marketing teams is |

strongly preferred. |

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SKILLS | Must Have |

- Advanced SQL |

- Strong Excel |

- Strong BI/dashboarding capability |

- Strong analytical and statistical |

thinking |

- Data manipulation and analysis |

- Customer/cohort analytics |

- Funnel analytics |

Preferred |

- Python |

- App analytics |

- Marketing attribution platforms |

- Data warehouse concepts |

- ETL/data pipeline understanding |

- A/B testing |

- Predictive analytics |

- Machine learning fundamentals |

- AI tools for analytics |

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WORKING CONDITIONS | Office / Hybrid (as per business |

requirement) |

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TRAVEL REQUIREMENTS | Need-based |

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TOOLS &

CERTIFICATIONS | - Power BI / Tableau / Looker |

- GA4 |

- MoEngage |

- Shopify |

- Google Ads |

- Clarity / Content Square |

- Mixpanel / Amplitude |

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Success Measures

1 Business decisions influenced by analytics. Number and impact of analytical recommendations implemented

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2 Revenue/growth opportunities identified

3 Customer retention and LTV insights generated

4 Successful deployment of advanced analytics/AI use cases

5 Adoption of dashboards/analytical products

📌 AM/ DM - & CRO/CX Alliances & Partnership (Bengaluru)
🏢 Jockey
📍 Bengaluru