finding-data-scientists-on-twitter

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English

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Chinese

Finding Data Scientists And Ml Engineers on Twitter

在Twitter上寻找数据科学家与ML工程师

Discovers data scientists and ML engineers on Twitter via skill keywords, portfolio/project signals, and open-to-work indicators. Twitter surfaces professionals who actively discuss their craft — a strong passive candidate signal.

通过技能关键词、作品集/项目信号以及求职意向标识，在Twitter上发掘数据科学家与ML工程师。Twitter上会展示那些积极讨论专业领域的从业者——这是很强的被动候选人信号。

Prerequisites

前提条件

```
APIFY_TOKEN
```
environment variable set
Optional: Apify MCP server installed

设置
```
APIFY_TOKEN
```
环境变量
可选：安装Apify MCP服务器

Inputs

输入参数

Parameter	Type	Required	Default	Notes
`startUrls`	array	Optional	`[]`	Twitter profile or tweet URLs
`twitterHandles`	array	Optional	`[]`	Twitter usernames (without @)
`twitterUserIds`	array	Optional	`[]`	Twitter user IDs
`getFollowers`	boolean	Optional	`false`	Extract follower lists
`getFollowing`	boolean	Optional	`false`	Extract following lists
`getRetweeters`	boolean	Optional	`false`	Extract retweeters of a tweet URL
`includeUnavailableUsers`	boolean	Optional	`false`	Include unavailable/suspended users
`maxItems`	number	Optional	Unlimited	Maximum users to return
`customMapFunction`	string	Optional	—	JavaScript function to transform each output object

参数	类型	是否必填	默认值	说明
`startUrls`	array	可选	`[]`	Twitter个人主页或推文URL
`twitterHandles`	array	可选	`[]`	Twitter用户名（不带@）
`twitterUserIds`	array	可选	`[]`	Twitter用户ID
`getFollowers`	boolean	可选	`false`	提取粉丝列表
`getFollowing`	boolean	可选	`false`	提取关注列表
`getRetweeters`	boolean	可选	`false`	提取推文的转发者列表
`includeUnavailableUsers`	boolean	可选	`false`	包含不可用/被封禁的用户
`maxItems`	number	可选	无限制	返回的最大用户数量
`customMapFunction`	string	可选	—	用于转换每个输出对象的JavaScript函数

Workflow

工作流程

Progress:
- [ ] Step 1: Search for role-specific tweets
- [ ] Step 2: Collect unique handles
- [ ] Step 3: Enrich profiles
- [ ] Step 4: Score candidate fit
- [ ] Step 5: Deliver candidate list

进度:
- [ ] 步骤1：搜索岗位相关推文
- [ ] 步骤2：收集唯一账号昵称
- [ ] 步骤3：完善个人资料
- [ ] 步骤4：候选人匹配度评分
- [ ] 步骤5：交付候选人列表

Step 1: Search Queries

步骤1：搜索查询

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

bash

undefined

推荐方式 — run_actor.js（自动处理等待、输出和文件保存）：

bash

undefined

Quick answer (prints table to chat)

快速查看（在聊天窗口打印表格）

node scripts/run_actor.js
--actor "apidojo~twitter-user-scraper"
--input '{"param": "value"}'

Save as CSV

保存为CSV格式

node scripts/run_actor.js
--actor "apidojo~twitter-user-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.csv --format csv

Save as JSON

保存为JSON格式

node scripts/run_actor.js
--actor "apidojo~twitter-user-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.json --format json

> `APIFY_TOKEN` must be set in environment or `.env` file.

**If Apify MCP is available:**

Tool: apify:run-actor Actor: "apidojo~tweet-scraper" Input: { "searchTerms": ["data scientist", "ML engineer", "LLM engineer", "machine learning open to work"], "maxItems": 300, "tweetLanguage": "en" }


**REST API fallback:**
```bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchTerms": ["data scientist", "ML engineer", "LLM engineer", "machine learning open to work"], "maxItems": 300}'

Collect unique

author.username

from results.

node scripts/run_actor.js
--actor "apidojo~twitter-user-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.json --format json

> 必须在环境变量或`.env`文件中设置`APIFY_TOKEN`。

**如果Apify MCP可用：**

工具: apify:run-actor Actor: "apidojo~tweet-scraper" 输入: { "searchTerms": ["data scientist", "ML engineer", "LLM engineer", "machine learning open to work"], "maxItems": 300, "tweetLanguage": "en" }


**REST API备选方案：**
```bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchTerms": ["data scientist", "ML engineer", "LLM engineer", "machine learning open to work"], "maxItems": 300}'

从结果中收集唯一的

author.username

。

Step 2: Enrich Profiles

步骤2：完善个人资料

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input: {"usernames": ["[username1]", "[username2]", "..."]}

REST API fallback:

bash

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"usernames": ["handle1", "handle2"]}'

如果Apify MCP可用：

工具: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
输入: {"usernames": ["[username1]", "[username2]", "..."]}

REST API备选方案：

bash

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"usernames": ["handle1", "handle2"]}'

Step 3: Filter and Score

步骤3：筛选与评分

Skill confirmation: bio contains keywords: "data science", "machine learning", "ML", "NLP", "LLM", "AI", "neural network", "PyTorch", "TensorFlow", "scikit-learn"

research_signal = bio contains 'PhD', 'researcher', or links to papers/Google Scholar

Candidate score:

candidate_score = (skill_confirmed ? 1 : 0) * 0.35
                + (open_to_work_signal ? 1 : 0) * 0.30
                + (followerCount in 200..20000 ? 1 : 0.6) * 0.20
                + (tweeted_in_last_30_days ? 1 : 0) * 0.15

Activity: active (< 30 days) | passive (30–90 days) | dormant (> 90 days)

技能确认： 个人简介包含以下关键词："data science"、"machine learning"、"ML"、"NLP"、"LLM"、"AI"、"neural network"、"PyTorch"、"TensorFlow"、"scikit-learn"

研究信号 = 个人简介包含'PhD'、'researcher'，或链接到论文/Google Scholar

候选人评分：

candidate_score = (skill_confirmed ? 1 : 0) * 0.35
                + (open_to_work_signal ? 1 : 0) * 0.30
                + (followerCount in 200..20000 ? 1 : 0.6) * 0.20
                + (tweeted_in_last_30_days ? 1 : 0) * 0.15

活跃度：活跃（<30天）| 被动（30–90天）| 休眠（>90天）

Step 4: Edge Cases

步骤4：特殊情况处理

Company/brand accounts in results: Filter where
```
followerCount > 50K
```
AND bio contains no personal pronouns; these are likely brand accounts
< 20 candidates found: Broaden skill term; remove location or seniority filter; try adjacent skills

Bot detection: Flag

followerCount / followingCount < 0.05

AND

tweetsCount < 20

as potential bot

Location not matching: Bio location is free text — use fuzzy match; accept partial city/country names

结果中出现企业/品牌账号：筛选掉粉丝数
```
followerCount > 50K
```
且个人简介不含人称代词的账号；这些很可能是品牌账号
找到的候选人不足20名：拓宽技能关键词；移除地点或资历筛选条件；尝试相关技能关键词
机器人检测：标记
```
followerCount / followingCount < 0.05
```
且
```
tweetsCount < 20
```
的账号为潜在机器人
地点不匹配：个人简介中的地点为自由文本——使用模糊匹配；接受部分城市/国家名称

Output Format

输出格式

undefined

undefined

Data Scientists And Ml Engineers Candidates: [ML_SPECIALTY]

数据科学家与ML工程师候选人：[ML专业方向]

Profiles found: [N] | Open-to-work: [N] | Active: [N] | Date: [DATE]

找到的资料数量：[N] | 求职意向：[N] | 活跃状态：[N] | 日期：[DATE]

Priority: Open-to-Work Candidates

优先级：有求职意向的候选人

Name	@Handle	Specialty	Location	Followers	Last Active	Score

姓名	@账号昵称	专业方向	地点	粉丝数	最后活跃时间	评分

Passive Candidates

被动候选人

Name	@Handle	Specialty	Location	Followers	Score

姓名	@账号昵称	专业方向	地点	粉丝数	评分

Bio Highlights (Top 5)

个人简介亮点（前5名）

@[handle]: "[bio excerpt]"

undefined

@[账号昵称]: "[个人简介节选]"

undefined

Troubleshooting

故障排除

All results are agencies/companies not individuals: Add personal pronouns filter or search

"I am a [role]"

"I do [skill]"

. Role too generic returns too many results: Add location OR seniority qualifier. No open-to-work signals: Most candidates don't signal publicly — treat passive candidates as warm leads with personalized outreach referencing their recent content.

所有结果均为机构/企业而非个人：添加人称代词筛选条件，或搜索

"I am a [role]"

、

"I do [skill]"

这类关键词。 岗位关键词过于宽泛导致结果过多：添加地点或资历限定条件。 无求职意向信号：大多数候选人不会公开表明求职意向——将被动候选人视为潜在对象，结合他们近期发布的内容进行个性化沟通。