Experience: 2.00 + years
Salary: USD 18000-30000 / year (based on experience)
Expected Notice Period: 15 Days
Shift: (GMT+05:30) Asia/Kolkata (IST)
Opportunity Type: Remote
Placement Type: Full Time Permanent position(Payroll and Compliance to be managed by: Premium cannabis brand redefining clean, elevated experiences through creativity and innovation.)
(*Note: This is a requirement for one of Uplers client - Premium cannabis brand redefining clean, elevated experiences through creativity and innovation.)
What do you need for this opportunity
Must have skills required:
PowerBI, LLM APIs, Make, n8n, SQ, Zapier, Python, Tableau
Premium cannabis brand redefining clean, elevated experiences through creativity and innovation. is Looking for:
About Crystal Clear California Crystal Clear California is a premium cannabis vape brand distributing across the United States. The company manufactures and sells disposable vapes and 510-thread cartridges through a network of licensed retail cannabis dispensaries. Crystal Clear operates with a direct sales model backed by a large freelance sales force, supplying product to over 300 retail stores and generating approximately $200,000 in monthly revenue. The business is now at an inflection point. With 300+ retail partners, 10+ direct sales representatives, and 30 freelancers managing those relationships, Crystal Clear has more sales data than it can process manually. The next phase of growth requires someone who can unlock that data, build intelligence systems around it, and use those insights to drive measurable revenue growth. The Role You will work directly with Doug Helfman (CEO) and the broader sales team. Your mandate is threefold: Build the data and AI infrastructure that Crystal Clear currently lacks Surface insights that improve sales rep performance across 300+ retail accounts Personally execute growth initiatives including outreach to underperforming and dormant accounts (post Month 3) What This Role Is — And Is Not Responsibilities by Phase Data Audit & Centralization Map all available data sources: retail sales, inventory levels, order frequency, pricing, rep activity logs, product mix by store Identify what data is clean, what is inconsistent, and what is missing entirely Centralize into a single source of truth that Doug and reps can trust Master Dashboard Build a live dashboard covering all 300+ retail accounts showing: Sales volume by store (weekly and monthly trend) Product mix: which SKUs are selling where Inventory velocity: products flying vs. stagnant Rep territory performance: total sales, active store count, variance between reps Churned and at-risk accounts: stores with declining or zero activity Automated Insights Report Weekly automated report delivered to Doug covering: Top 10 performing stores and what is driving their results Bottom 10 underperformers with hypotheses on root cause Accounts at risk of going dark in the next 30 days Fastest-moving and slowest-moving SKUs Sales Rep Territory Dashboards Individual dashboards for each rep showing their stores vs. the portfolio average Product recommendations per store: what should they be pushing this week Inventory alerts: which stores need a call today Store Opportunity Scoring Build an AI-driven model scoring each of the 300+ stores on growth potential Inputs: current sales vs. theoretical capacity, local demographics, product penetration, inventory turnover rate Output: prioritized list of stores with highest untapped revenue potential Churn Risk Prediction Identify which stores are likely to go inactive before it happens Inputs: order frequency, velocity trends, last order date, rep engagement frequency Output: automated alerts to reps when a store crosses a risk threshold, with suggested intervention Product Recommendation Engine Per-store product recommendations based on historical category performance Example output: 'Store #42 over-indexes on disposables vs. the market — offer the cartridge line with a trial allocation' Rep Performance Analysis Identify activity patterns that correlate with higher revenue per territory Surface coaching insights: what behaviors do top-performing reps share that underperformers do not Cold Outreach Campaigns Target: dormant accounts, non-responding stores, and high-potential prospects not currently in the network Use AI to personalize outreach at scale: each email references that store's specific data, product fit, or market opportunity 30–40 outreach touchpoints per week track open rates, reply rates, and conversion to meetings Hand qualified opportunities to Doug or the relevant rep for close Sales Rep Enablement (Ongoing) Weekly insights calls with Doug and senior reps: 'Here is what the data shows, here is what we should do' Real-time alerts when stores need immediate attention Ongoing iteration on dashboards based on rep feedback Strategic Growth Projects Market analysis: which geographies or store types are growing vs. declining Seasonal campaign planning: what to push, when, and to whom, based on historical data New product launch support: which stores get priority allocation and why What We Are Looking For Strong Agentic AI Experience — Primary Requirement This is the non-negotiable foundation of the role. We are not looking for someone who has experimented with ChatGPT or completed AI courses. We need someone who has built real, working agentic systems that solved real problems. Hands-on experience building AI agent workflows using LLM APIs (OpenAI, Claude, Gemini, or similar) Experience with no-code or low-code automation platforms: Make.com, n8n, Zapier, or equivalent, integrated with AI outputs Demonstrated prompt engineering: iterating on instructions to achieve reliable, structured outputs at scale Understands AI limitations: hallucinations, output variability, quality control builds systems that account for these Has shipped at least one end-to-end agentic workflow that produced measurable value Portfolio required: be prepared to walk through exactly what you built, why, what broke, and how you fixed it Strong Analytical & Statistical Thinking You do not need a data science degree. You need to think rigorously about data: what it tells you, what it does not, and how confident you should be in your conclusions. Comfort working with large, messy datasets: cleaning, structuring, identifying anomalies Ability to translate business questions into data questions, and data findings into business recommendations Experience with BI or visualization tools: Tableau, Power BI, Looker, or Google Data Studio Familiarity with sales and revenue metrics: funnel conversion, churn, order frequency, cohort analysis Knows the difference between correlation and causation does not overclaim from limited data SQL or Python is a plus, not a requirement Open to Growth Marketing and Sales Execution From Month 3 onward, a meaningful portion of your time will shift toward hands-on revenue activities. You do not need a sales background, but you must be genuinely open to owning that work. Comfortable writing cold emails in clear, persuasive English: not templated, but data-informed and store-specific Willing to run outreach campaigns and iterate based on response data Interested in the full loop: building the insight, acting on it, measuring what happens, and improving Treats sales execution as a growth experiment, not a chore Mindset & Ways of Working Ships fast, learns faster: gets a working version out, measures, improves Comfortable with ambiguity: does not wait for perfect requirements before moving Builds for business impact, not technical elegance Communicates clearly: can explain a complex analysis to Doug in two minutes Works independently across a split timezone: disciplined, async-friendly, proactive with updates Curious by default: asks why before jumping to how Nice to Have Experience with sales operations, revenue operations, or B2B distribution analytics Prior exposure to cannabis, CPG, FMCG, or multi-location retail environments Python or SQL for data manipulation beyond what BI tools offer Experience with CRM platforms: HubSpot, Salesforce, or similar Background in growth marketing, lifecycle campaigns, or conversion rate optimization Prior experience working with or for early-stage startups or small teams Working Arrangement 90-Day Success Roadmap What Success Looks Like at 6 Months A live dashboard used weekly by Doug and all 40+ reps — not one that exists but no one checks Automated insights report that drives Doug's Monday planning, not just something he reads and files away Rep performance variance explained and being coached: top-rep behaviours are being replicated Store opportunity scores guiding which accounts get priority attention each quarter Churn risk alerts catching at-risk stores 30+ days before they go dark Cold outreach campaign generating new distribution partners at a measurable rate Doug making decisions based on data, not gut — and asking you 'what does the data say' as a first instinct
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