6sense
6sense2 open roles

Senior Data Scientist, Forward Deployed

$168–230kUnited StatesRemoteSeniorFull-time

Posted 3 days ago

The role in short

Embed with strategic customers to tailor, retrain, and validate predictive sales and marketing models on their production data, driving business value and productizing successful patterns.

Our Mission:

6sense's mission is to multiply what matters: growth, retention, and efficiency. We envision a future where companies, teams and people reach their full potential.

Our People:

People are the heart and soul of 6sense. We serve with passion and purpose. We live by our Being 6sense values of Win as One Team, Stay Curious, Do The Right Thing, Own the Outcome, and Create Belonging. Every 6sensor plays a part in defining the future of our industry-leading technology. 6sense is a place where difference-makers roll up their sleeves, take risks, act with integrity, and measure success by the value we create for our customers. We want 6sense to be the best chapter of your career.

Senior Data Scientist, Forward Deployed

Forward Deployed Engineering · Predictive track · Senior

TEAM

Forward Deployed Engineering (FDE), Predictive track, in Data Science & AI Engineering

REPORTS TO

Director of Data Science

LEVEL

Senior (5+ years) — the team's lead modeler

LOCATION

United States

THE ROLE IN ONE PARAGRAPH

We're hiring a Senior Data Scientist to be the team's lead modeler. You'll work hands-on with our strategic customers' data to tailor, retrain and validate the models that tell their sales and marketing teams which accounts and buyers to focus on.

You'll usually drive two or three customer engagements at once, each on real production data, and present the results in terms of business value. The best of your work becomes a standard capability for every 6sense customer.

What You'll Do

Build for the customer

  • Get to know each customer's go-to-market and data, from CRM and marketing automation to firmographic and intent signals.
  • Retrain and tune account and contact models: ideal customer profile and industry definitions, keyword strategy, how CRM activity feeds the models, feature selection, and the balance of first- and third-party signals.
  • Prove impact with backtests against the live model and, where possible, the customer's own scoring, using opportunity conversion by score bucket.
  • Calibrate score thresholds and scaling so customers get a steady, explainable flow of qualified accounts.
  • Build customer-ready deliverables: review decks, explainability views, score files and documentation customers can verify.
  • Partner with the team's MLOps engineer to bring solutions into production so they keep delivering after handoff.

Productize for everyone

  • Capture what you learn as reusable patterns, and share recurring needs with Product, backed by field evidence.

What You'll Bring

  • Required: 6-8 years building and deploying machine learning models in production, ideally ranking, propensity, classification or scoring models on tabular or behavioral data.
  • Strong foundation in machine learning and applied statistics, including tree-based models such as XGBoost and Random Forest, model evaluation and calibration.
  • Fluent in Python and SQL, with experience on large, messy, real-world datasets, including matching customer CRM records to account data.
  • Experience working with or for customers on their data: consulting, client-site work, or a forward-deployed, solutions or customer-facing data role.
  • Comfortable switching between several ad hoc projects in a week rather than one long roadmap item.
  • Able to explain technical work clearly to non-technical audiences.
  • A product mindset: you look for the solution that works for the next ten customers, not just this one.
  • Nice to have: B2B go-to-market data (CRM, marketing automation, intent, ABM or predictive scoring); lead or contact scoring models; model explainability methods such as feature importance or SHAP; Spark, SingleStore or Hive on AWS; LLM-based tools, agents or evaluation methods.

What Success Looks Like

  • In 3 months: you've delivered tailored models for at least two customer engagements, each with before-and-after results.
  • In 6 months: at least one of your solutions is reusable across customers and on its way into the product.
  • In 12 months: you're the team's go-to expert on model tuning and validation, and your playbooks make each new engagement faster.

Why this role is different: you work on real customer data with real stakes, see the results in the customer's pipeline, and your best work becomes a capability every 6sense customer uses.

Base Salary Range: $168,349.50 - $230,912.60. The base salary range represents the anticipated low and high end of the base salary range for this position. Actual salaries may vary and may be above or below the range based on various factors, including but not limited to work location and experience. The base salary is one component of 6sense’s total compensation package for this position. Other compensation may include a bonus program or commission plan, and stock options if approved by 6sense’s board. In addition, 6sense provides a variety of benefits, including generous health insurance coverage, life, and disability insurance, a 401K employer matching program, paid holidays, self-care days, and paid time off (PTO). #Li-remote

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Our Benefits:

Full-time employees can take advantage of health coverage, paid parental leave, generous paid time-off and holidays, quarterly self-care days off, and stock options. We’ll make sure you have the equipment and support you need to work and connect with your teams, at home or in one of our offices.

We have a growth mindset culture that is represented in all that we do, from onboarding through to numerous learning and development initiatives including access to our LinkedIn Learning platform. Employee well-being is also top of mind for us. We host quarterly wellness education sessions to encourage self care and personal growth. From wellness days to ERG-hosted events, we celebrate and energize all 6sense employees and their backgrounds.

Equal Opportunity Employer:

6sense is an Equal Employment Opportunity and Affirmative Action Employers. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status. If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to jobs@6sense.com.

We are aware of recruiting impersonation attempts that are not affiliated with 6sense in any way. All email communications from 6sense will originate from the @6sense.com domain. We will not initially contact you via text message and will never request payments. If you are uncertain whether you have been contacted by an official 6sense employee, reach out to jobs@6sense.com

Prep for this interview

2 questions likely to come up at 6sense, each matched to a line in this posting.

  1. 1Data and SQL
    They wrote: “Fluent in Python and SQL, with experience on large, messy, real-world datasets, including matching customer CRM records to account data.”

    Write a query that finds each customer's first and most recent order.

    How to answer

    Why they ask: A quick check that you're comfortable with grouping, joins and window functions.

    1. Clarify the tables and what counts as an order (cancelled ones?)
    2. Use MIN/MAX with GROUP BY, or ROW_NUMBER() if you need the whole row
    3. Handle ties on the same timestamp, and customers with no orders
    4. Explain how you'd test it on a few known customers

    Tip: Say what you'd check in the data before writing the query: duplicates, time zones, nulls.

  2. 2Coding
    They wrote: “Get to know each customer's go-to-market and data, from CRM and marketing automation to firmographic and intent signals.”

    Build a small in-memory cache. Then add expiry. Then a size limit.

    How to answer

    Why they ask: Staged exercises show how your code holds up when requirements change mid-interview, like at a customer.

    1. Start with get and set on a dictionary, plus tests
    2. Store an expiry time with each value and check it on read
    3. Track use order and evict the least recently used entry when full
    4. Say what you'd change for many threads or many machines

    Tip: Keep the first version simple and well tested so the next stage is a small change.

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