Sales Development Representative

Overview

You generate pipeline by prospecting into Fortune 500 accounts with large data science teams. You're researching companies, finding the right people (VPs of Data Science, ML Engineering leaders), and trying to get them on the phone or to respond to emails. Most ignore you. Your goal is booking 12-15 qualified discovery meetings per month for AEs. This is a grind—lots of calls, lots of rejection, and you need to sound credible talking about MLOps even though you're early in your career.

Role Snapshot

Company Context

Stage: Later-stage (248 employees)

Size: 248 employees

Market: Selling into technical buyers (data scientists, ML engineers) at enterprises

Challenge: Your prospects get hit up by every dev tool and data vendor constantly—breaking through is hard

What You'll Actually Do

Time Breakdown

Cold Calling (40%) | Email/LinkedIn Outreach (30%) | Research (15%) | Meeting Coordination (10%) | Training/Admin (5%)

Key Activities

• Account Research: You're given a list of target accounts (large enterprises with data science teams). You research them—How many data scientists do they have? What AI initiatives are public? Who runs their ML org? You use LinkedIn, company websites, news articles.
• Cold Calling: You make 50-70 calls per day to VPs of Data Science, Directors of ML Engineering, Chief Data Officers. Most don't answer. Many hang up quickly. You're trying to pique interest with a 30-second pitch about helping them deploy models faster.
• Email Sequences: You run cadences—multi-touch email sequences over 2-3 weeks. Subject lines matter. Personalization matters. Most still don't respond. You're constantly testing messaging.
• LinkedIn Outreach: You connect with prospects on LinkedIn, comment on their posts, send InMails. Trying to build familiarity before calling.
• Inbound Lead Follow-Up: Some leads come in from webinars, content downloads, demo requests. You call them within 5 minutes (speed-to-lead matters). Even inbound leads ghost you half the time.
• Meeting Handoffs: When you book a meeting, you brief the AE on what you learned, attend the first 5 minutes, then drop off. AE takes over from there.

The Honest Reality

What's Hard

• Low response rates: You call 70 people, maybe 5 pick up, maybe 1 agrees to a meeting. That's a good day. Most days are worse.
• Technical intimidation: Prospects are senior data scientists. You're early in your career. They can sense if you don't understand MLOps. You need to learn enough to sound credible fast.
• Gatekeepers: Executive admins screen calls. "What's this regarding?" If you fumble, you're not getting through.
• Ghosting: Prospect says "send me some info," you do, then they vanish. You follow up 5 times. Still nothing. This is most interactions.
• Repetitive work: Same pitch, same objections, same rejections, 50+ times per day. It's mentally draining.
• Activity pressure: You're tracked on calls, emails, meetings booked. Missing activity targets gets flagged fast. It's a volume game.

What Success Looks Like

• You book 12-15 qualified meetings per month consistently
• Your meetings convert to pipeline at 50%+ (meaning AEs don't reject them as unqualified)
• You learn the product and market well enough to have credible conversations
• You develop patterns—which accounts respond, which messaging works, which times of day get pickups
• You get promoted to AE within 12-18 months

Who You're Calling

Primary Targets:

• VP/Director of Data Science
• VP/Director of ML Engineering
• Chief Data Officer
• Head of AI

What They Care About:

• Not wasting time on irrelevant sales pitches
• Solving real pain (slow model deployment, lack of governance, scaling challenges)
• Vendors who understand their technical world

Common Responses:

• "We're happy with our current setup" (even if they're not)
• "Send me an email" (then they ignore it)
• "We're not looking right now" (code for "not interested")
• Hang-ups, no-answers, straight to voicemail

Requirements

• 0-2 years in sales (this is often a first sales job)
• Grit and resilience—you need thick skin for constant rejection
• Willingness to learn technical concepts (MLOps, model deployment, Kubernetes basics)
• Strong communication skills—you're often talking to PhDs and senior engineers
• High activity tolerance—this is a volume role, you're grinding daily
• Coachable—you need to take feedback and iterate on your approach constantly