GTM WORK & BACKGROUND
Prospecting, enrichment, outbound operations, and automation, followed by earlier engineering and research projects. Each story describes my contribution and its current scope.
Showing 16 projects
Collect relevant social activity and distinguish events from unique professional identities.
Reconcile delivery and check identity, company fit, roles, and duplicates.
Return a coherent dataset with review queues and clear next steps for contact qualification.
Reconciling captured activity, repairing delivery, and separating potential buyers from research-only records.
Identify missing profiles, inconsistent identities, and work-contact gaps.
Recover relevant data and cross-check identity and company affiliation.
Return richer professional records with unresolved cases separated for review.
Improving professional-contact coverage while keeping affiliation and verification evidence explicit.
Read customer context and lifecycle information to choose a relevant communication path.
Apply segment rules, ownership checks, exclusions, and consistent field mappings.
Deliver reviewable destinations while keeping held records out of downstream preparation.
Connecting segments to campaign workflows with explicit ownership, suppression, and merge-field rules.
Review mailbox configuration, DNS authentication, bounce evidence, and sender warnings.
Apply agreed settings and isolate confirmed issues while preserving operational history.
Verify saved settings and assignments and clearly hand over any remaining provider repairs.
Auditing sending settings and excluding confirmed flagged inbox assignments while retaining operational history.
Start with the offer, audience, approved messaging, and required sender fields.
Build sequences with personalization, sentence variants, and contextual follow-ups.
Inspect rendered messages and saved drafts before final review.
Preparing outreach variants, follow-ups, and dynamic fields with repeatable rendering checks.
Collect campaign counters and conversation labels with consistent definitions.
Read conversations alongside the exact messages recipients received.
Report clear interest, deferred interest, and acknowledgements separately.
Reconciling campaign labels with real conversations and the exact messages recipients received.
Build a consistent inventory of campaigns and sender assignments.
Check actual assignments and shared mailbox limits.
Provide a realistic assessment and change assignments only where needed.
Checking mailbox assignments across campaigns and accounting for shared sender limits.
Combine customer activity, contact information, and evidence of fit.
Apply transparent scoring rules and keep missing information visible.
Deliver ranked records for the team’s existing review workflow.
Turning product usage and activity into transparent, reviewable customer priorities.
Collect overlapping records and identify the fields that must stay consistent.
Resolve identities and apply explicit segment and exclusion rules.
Create clean destinations with repeatable updates and preserved source fields.
Consolidating customer data into useful cohorts while preserving identity, provenance, and business rules.
Preserve original names and distinguish given-name from full-name inputs.
Apply shared JavaScript, Python, and Clay rules, preserving accents and compound names.
Produce consistent greeting fields verified through regression checks.
Consistent name normalization across local processing and Clay, with source identities preserved.
Model activity, repeated events, identity changes, opt-outs, and account ownership.
Build durable state and reservation logic with review and recovery paths.
Validate synthetic scenarios before any live collection or outreach.
An X-focused prototype with event deduplication, persistent ledgers, reservations, and human-review gates.
Review existing playbooks, scripts, and operating rules.
Connect resources through stable paths and consistent normalization and review checks.
Deliver maintainable workflows with source provenance and repeatable verification.
Integrating existing playbooks and adding consistent normalization, client-assignment, and review rules.
Inventory assets, paths, sizes, and hashes before changes.
Organize the archive, repair active references, and retain original versions.
Verify migrated files and dependencies and provide a traceable record.
Restructuring a working archive while preserving file integrity, references, and dated evidence.
Identify source systems and the data contracts between them.
Implement transformations and coordinate database and API interfaces.
Deliver connected data flows that fit the surrounding system.
Data pipelines and API coordination across connected enterprise environments.
Prepare experimental datasets and comparable classification tasks.
Compare gradient and block-coordinate strategies under explicit conditions.
Document accuracy and computational cost alongside the implementation.
Collaborative research comparing full and block-coordinate gradient methods for multiclass logistic regression.
Prepare dataset features and inspect modelling assumptions.
Experiment with neural-network and regression approaches.
Compare model behaviour and visualize relationships explaining the results.
Academic experiments with neural networks and linear and polynomial relationships in data.
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