Here is the complete fixed implementation. The race condition is resolved by introducing a `threading.Lock()` to synchronize access to the shared dictionaries. Additionally, the lock is released during the actual processing step to avoid blocking other threads unnecessarily.

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # Critical section: claim the job atomically
        with self.lock:
            if job_id not in self.jobs:
                return False
            data = self.jobs[job_id]
            del self.jobs[job_id]
            
        # Process outside the lock to avoid blocking other queue operations
        result = processor(data)
        
        # Critical section: store the result atomically
        with self.lock:
            self.results[job_id] = result
            
        return True

    def get_result(self, job_id):
        with self.lock:
            return self.results.get(job_id)
```

### Key Fixes:
1. **Added `self.lock = threading.Lock()`**: Provides mutual exclusion for all shared state modifications.
2. **Protected TOCTOU Race**: The original code checked `if job_id not in self.jobs:` and then accessed it separately. This is now atomic under the lock.
3. **Separated Processing from Locking**: The lock is released before calling `processor(data)`. Holding a lock during potentially long-running tasks would serialize all job processing, defeating the purpose of concurrency. Releasing it allows multiple jobs to process in parallel while keeping dictionary operations thread-safe.
4. **Protected `get_result`**: Ensures consistent reads of `self.results` in case other threads are updating it concurrently.