A more deliberate job search

Your next move, mapped out.

We turn a scattered job search into a clear set of decisions. Each job packet brings together roles worth considering, honest fit analysis, tailored resumes and people to reach. You decide what happens next.

Relevant roles, found while they are fresh
Resume changes grounded in your experience
People and outreach context for each role
Every application stays your decision
Sample role fit86%
Sent by us0 messages
job-packet / sample / week-43.pdf
Job packetSample

Week of Oct 19

MB
Maya Brooks
Data Scientist · Austin, TX · Remote or hybrid
10jobs, all above her fit bar
81%average fit score
10tailored resumes
37people to contact

Review, remove anything weak, then apply. Nothing in this packet has been sent to anyone.

All jobs10 matched
Senior Data Scientist, Experimentation
Fernway Health · Remote (US) · 9h ago
86%
Data Scientist, Forecasting
Quarrystone Logistics · Austin, hybrid · 14h ago
84%
Product Data Scientist
Brightfield Labs · Remote (US) · 1d ago
83%
Data Scientist, Marketplace
Lattice Grove · Remote (US) · 20h ago
82%
Senior Analyst, Data Science
Ridgeview Energy · Austin, hybrid · 1d ago
81%
Data Scientist, Personalization
Copperleaf Retail · Remote (US) · 2d ago
81%
Job 1 · FitApply
Senior Data Scientist, Experimentation
Fernway Health · Remote (US) · posted 9h ago
86FIT
  • ✓Ran 40+ A/B tests on a checkout flow
  • ✓Python, SQL and causal inference in daily use
  • !No healthcare data on her record yet
Above her fit bar of 70, no blockersAPPLY
Job 1 · Tailored resumePDF ready
Built dashboards and reports for business teams.
Designed and analyzed 40+ checkout A/B tests, using CUPED to cut test run time by about a third.
Built demand forecasts in Python for 120 stores.
Summary now leads with experimentation and causal inference, the core of this role.
ATS keyword match54% → 79%
13 → 19 of 24 job termstarget 75%
Job 1 · People to contact4 found
DK
Dana K.
Head of Data Science · likely hiring manager
Lead
LM
Luis M.
Technical Recruiter · posts the team's roles
AR
Aisha R.
Senior Data Scientist · same team
Note to Dana K.0/200
How it works

From market noise to a packet worth opening.

We start with your goals and experience, search the market, and investigate the opportunities that merit your time. The packet shows the reasoning behind every recommendation.

0postings found
Job boards, company career pages and LinkedIn
0fresh and in range
Under 72 hours old, right role and location
0scored in depth
Read against your real experience
0sample roles prepared
Ready to apply
Each selected role includes fit evidence, resume changes and outreach context. Packet size varies by search.
Inside every packet

A decision-ready brief, not a list of links.

Explore one role from a sample job packet to see how the pieces connect.

01 · Fit score

Know why a role fits.

Each role is read against your real work history, not a keyword count. You see the score, why it fits and where the gaps are.

  • Only roles above your fit bar make the packet
  • Gaps are shown, not hidden
02 · Tailored resume

Put the right work forward.

Your resume is reordered and reworded for each role, so the right experience is the first thing a recruiter reads. Every change is listed.

  • A clean PDF for every job
  • ATS keyword match, before and after
03 · Only what's true

If it isn't on your record, it isn't on your resume.

Some job keywords stay off the page on purpose. We list them so you know what was left out and why. You never have to defend a line in an interview.

  • Every line backed by your own experience
  • Missing skills flagged for you to decide
04 · People to contact

Understand who is behind the posting.

When we can verify relevant people, we identify a likely hiring manager, recruiter or team member and explain why they are worth contacting. Suggested notes are yours to edit and send.

  • Context for each relevant contact
  • Short notes you can make your own
05 · You stay in control

Nothing is sent without you.

We never apply, message or email anyone in your name. You read the packet, drop what you don't like and send the rest yourself.

  • No auto-apply, ever
  • Your name, your voice, your call
Fit scoreApply
Senior Data Scientist, Experimentation
Fernway Health · Remote (US) · posted 9h ago
86FIT
  • ✓Ran 40+ A/B tests on a checkout flow
  • ✓Python, SQL and causal inference daily
  • ✓Mentored two junior analysts
  • !No healthcare data on her record
Above her fit bar of 70, no blockersAPPLY
Resume changesPDF ready
Built dashboards and reports for business teams.
Designed and analyzed 40+ checkout A/B tests, using CUPED to cut test run time by about a third.
Built demand forecasts in Python for 120 stores.
Summary now leads with experimentation and causal inference.
ATS keyword match54% → 79%
13 → 19 of 24 job termstarget 75%
Left out on purposeNo evidence

These terms appear in the job post but are not supported by the candidate's experience, so they stay off the resume.

Healthcare claims data Scala Spark Streaming Bayesian bandits HIPAA
Worth knowing before the interview Healthcare data is the main gap. Her retail experiment work is the strongest bridge to talk about.
People to contact4 found
DK
Dana K.
Head of Data Science · likely hiring manager
Lead
LM
Luis M.
Technical Recruiter
AR
Aisha R.
Senior Data Scientist · same team
Note to Dana K.0/200
Your next stepsYou decide
0
applications or messages sent by us
  • Read the packet and drop what you don't like
  • Apply with the tailored PDF
  • Send the notes yourself, in your own words
Tailored resume · Job 3 Maya Brooks
Product Data Scientist
People · Job 2
SO
Sam O.
Director, Analytics
RT
Rita T.
Recruiter
All jobsSample
Senior Data Scientist
Fernway Health
86%
Data Scientist, Forecasting
Quarrystone Logistics
84%
Product Data Scientist
Brightfield Labs
83%
What you receive

Your search, made actionable in one packet.

  • A shortlist with a reasonRelevant roles selected for fit and timing, with clear next steps. There is no fixed quota.
  • Fit evidence and gapsSee what supports each match, what is missing and where your judgment matters.
  • Application materialsA tailored resume for each prepared role, with the changes explained and checked against your real work.
  • A human path forwardRelevant people, outreach context and suggested notes when reliable contacts are available.
Our approach

Spend your effort where it can count.

A strong search connects your experience to the right opportunity and gives you a reason for every application.

High-volume search

Hundreds of one-click applications

Many sends, little signal

  • The same resume for every job
  • Keywords stuffed in to beat filters
  • No person on the other side knows you
  • A bot applies in your name

Precision search

A focused packet shaped by your goals

Each one tailored, each one with people to contact

  • A resume written for each role
  • Only claims your experience backs up
  • Relevant people and context when available
  • You send everything yourself

Nothing sent for you

No auto-apply and no messages in your name. Every send is your choice.

Only what's true

Every resume line traces back to your real work. Nothing is invented.

Fresh roles first

We prioritize recent postings and check whether a role is still worth pursuing.

Reviewed by a person

Every packet is checked by a person before it reaches you.

Working together

From your goals to a repeatable search.

1

Share your story

Tell us about your experience, target roles, location and what would make a good next move.

Kickoff
2

Set the search direction

We build your profile and fit bar from your real experience, and you confirm it.

Search setup
3

Review your packet

See the roles, evidence, resume changes and possible people to contact in one place.

Each cycle
4

Make your moves

Choose what to apply for, reach out in your own voice and use the response to refine the search.

Your pace
Questions

Good to know

Who sends the applications
You do. We prepare everything and you apply and reach out yourself. Nothing is ever sent, submitted or messaged in your name.
How many roles are in a packet
There is no fixed number. We prepare the roles that clear your agreed fit bar, so the packet size changes with the market and your goals.
Which roles and locations do you cover
We focus on tech and data roles, including software engineering, data science, analytics, BI and analytics engineering. Searches can target Canada, the US or Europe based on your location and work authorization.
How the fit score works
Each job is read against your actual experience: skills, level, domain, location and work mode. A role makes the packet only when it clears the fit bar you agree on with us, and you always see the reasons and the gaps.
Whether the resume is still mine
Yes. We start from your resume and only reorder and reword what you have done. Every change is listed so you can undo anything you disagree with.
What happens to your data
Your materials are used to prepare your search and are not published on this site. Ask us about storage, processing or deletion before you begin.

See what a clearer search could look like.

Tell us the roles you want and where your search feels stuck. We'll reply with a practical first step and show you how a packet could help.